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charset=UTF-8 Content-Transfer-Encoding: 8bit --- units/ja/_toctree.yml | 82 +++ units/ja/unit0/introduction.mdx | 144 +++++ units/ja/unit1/architectural-components.mdx | 85 +++ units/ja/unit1/capabilities.mdx | 243 ++++++++ units/ja/unit1/certificate.mdx | 19 + units/ja/unit1/communication-protocol.mdx | 223 ++++++++ units/ja/unit1/gradio-mcp.mdx | 154 ++++++ units/ja/unit1/introduction.mdx | 33 ++ units/ja/unit1/key-concepts.mdx | 92 ++++ units/ja/unit1/mcp-clients.mdx | 357 ++++++++++++ units/ja/unit1/quiz1.mdx | 125 +++++ units/ja/unit1/quiz2.mdx | 125 +++++ units/ja/unit1/sdk.mdx | 179 ++++++ units/ja/unit1/unit1-recap.mdx | 45 ++ units/ja/unit2/clients.mdx | 136 +++++ units/ja/unit2/continue-client.mdx | 193 +++++++ units/ja/unit2/gradio-client.mdx | 176 ++++++ units/ja/unit2/gradio-server.mdx | 193 +++++++ units/ja/unit2/introduction.mdx | 64 +++ units/ja/unit2/tiny-agents.mdx | 271 +++++++++ .../build-mcp-server-solution-walkthrough.mdx | 444 +++++++++++++++ units/ja/unit3/build-mcp-server.mdx | 379 +++++++++++++ units/ja/unit3/certificate.mdx | 19 + units/ja/unit3/conclusion.mdx | 147 +++++ units/ja/unit3/github-actions-integration.mdx | 268 +++++++++ units/ja/unit3/introduction.mdx | 85 +++ .../unit3/mcp-server-hf-course-in-action.png | Bin 0 -> 2178155 bytes units/ja/unit3/slack-notification.mdx | 376 +++++++++++++ units/ja/unit3_1/conclusion.mdx | 42 ++ units/ja/unit3_1/creating-the-mcp-server.mdx | 336 ++++++++++++ units/ja/unit3_1/introduction.mdx | 108 ++++ units/ja/unit3_1/mcp-client.mdx | 370 +++++++++++++ units/ja/unit3_1/quiz1.mdx | 149 +++++ units/ja/unit3_1/quiz2.mdx | 173 ++++++ units/ja/unit3_1/setting-up-the-project.mdx | 133 +++++ units/ja/unit3_1/webhook-listener.mdx | 519 ++++++++++++++++++ 36 files changed, 6487 insertions(+) create mode 100644 units/ja/_toctree.yml create mode 100644 units/ja/unit0/introduction.mdx create mode 100644 units/ja/unit1/architectural-components.mdx create mode 100644 units/ja/unit1/capabilities.mdx create mode 100644 units/ja/unit1/certificate.mdx create mode 100644 units/ja/unit1/communication-protocol.mdx create mode 100644 units/ja/unit1/gradio-mcp.mdx create mode 100644 units/ja/unit1/introduction.mdx create mode 100644 units/ja/unit1/key-concepts.mdx create mode 100644 units/ja/unit1/mcp-clients.mdx create mode 100644 units/ja/unit1/quiz1.mdx create mode 100644 units/ja/unit1/quiz2.mdx create mode 100644 units/ja/unit1/sdk.mdx create mode 100644 units/ja/unit1/unit1-recap.mdx create mode 100644 units/ja/unit2/clients.mdx create mode 100644 units/ja/unit2/continue-client.mdx create mode 100644 units/ja/unit2/gradio-client.mdx create mode 100644 units/ja/unit2/gradio-server.mdx create mode 100644 units/ja/unit2/introduction.mdx create mode 100644 units/ja/unit2/tiny-agents.mdx create mode 100644 units/ja/unit3/build-mcp-server-solution-walkthrough.mdx create mode 100644 units/ja/unit3/build-mcp-server.mdx create mode 100644 units/ja/unit3/certificate.mdx create mode 100644 units/ja/unit3/conclusion.mdx create mode 100644 units/ja/unit3/github-actions-integration.mdx create mode 100644 units/ja/unit3/introduction.mdx create mode 100644 units/ja/unit3/mcp-server-hf-course-in-action.png create mode 100644 units/ja/unit3/slack-notification.mdx create mode 100644 units/ja/unit3_1/conclusion.mdx create mode 100644 units/ja/unit3_1/creating-the-mcp-server.mdx create mode 100644 units/ja/unit3_1/introduction.mdx create mode 100644 units/ja/unit3_1/mcp-client.mdx create mode 100644 units/ja/unit3_1/quiz1.mdx create mode 100644 units/ja/unit3_1/quiz2.mdx create mode 100644 units/ja/unit3_1/setting-up-the-project.mdx create mode 100644 units/ja/unit3_1/webhook-listener.mdx diff --git a/units/ja/_toctree.yml b/units/ja/_toctree.yml new file mode 100644 index 0000000..a863654 --- /dev/null +++ b/units/ja/_toctree.yml @@ -0,0 +1,82 @@ +- title: "0. MCPコースへようこそ" + sections: + - local: unit0/introduction + title: MCPコースへようこそ + +- title: "1. Model Context Protocolの紹介" + sections: + - local: unit1/introduction + title: Model Context Protocol (MCP)の紹介 + - local: unit1/key-concepts + title: 主要概念と用語 + - local: unit1/architectural-components + title: アーキテクチャコンポーネント + - local: unit1/quiz1 + title: クイズ1 - MCP基礎 + - local: unit1/communication-protocol + title: 通信プロトコル + - local: unit1/capabilities + title: MCP機能の理解 + - local: unit1/sdk + title: MCP SDK + - local: unit1/quiz2 + title: クイズ2 - MCP SDK + - local: unit1/mcp-clients + title: MCPクライアント + - local: unit1/gradio-mcp + title: Gradio MCP統合 + - local: unit1/unit1-recap + title: ユニット1まとめ + - local: unit1/certificate + title: 証明書を取得しよう！ + +- title: "2. ユースケース: エンドツーエンドMCPアプリケーション" + sections: + - local: unit2/introduction + title: MCPアプリケーション構築の紹介 + - local: unit2/gradio-server + title: Gradio MCPサーバーの構築 + - local: unit2/clients + title: アプリケーションでのMCPクライアントの使用 + - local: unit2/continue-client + title: AIコーディングアシスタントでのMCPの使用 + - local: unit2/gradio-client + title: GradioでのMCPクライアント構築 + - local: unit2/tiny-agents + title: MCPとHugging Face Hubを使ったTiny Agentsの構築 + +- title: "3. 高度なMCP開発: カスタムワークフローサーバー" + sections: + - local: unit3/introduction + title: Claude Code用カスタムワークフローサーバーの構築 + - local: unit3/build-mcp-server + title: "モジュール1: MCPサーバーの構築" + - local: unit3/github-actions-integration + title: "モジュール2: GitHub Actions統合" + - local: unit3/slack-notification + title: "モジュール3: Slack通知" + - local: unit3/build-mcp-server-solution-walkthrough + title: "ユニット3解答例: MCPを使ったプルリクエストエージェントの構築" + - local: unit3/certificate + title: "証明書を取得しよう！" + - local: unit3/conclusion + title: "ユニット3まとめ" + +- title: "3.1. ユースケース: Hub上でのプルリクエストエージェントの構築" + sections: + - local: unit3_1/introduction + title: Hugging Face Hub上でのプルリクエストエージェントの構築 + - local: unit3_1/setting-up-the-project + title: プロジェクトのセットアップ + - local: unit3_1/creating-the-mcp-server + title: MCPサーバーの作成 + - local: unit3_1/quiz1 + title: クイズ1 - MCPサーバー実装 + - local: unit3_1/mcp-client + title: MCPクライアント + - local: unit3_1/webhook-listener + title: Webhookリスナー + - local: unit3_1/quiz2 + title: クイズ2 - プルリクエストエージェント統合 + - local: unit3_1/conclusion + title: まとめ diff --git a/units/ja/unit0/introduction.mdx b/units/ja/unit0/introduction.mdx new file mode 100644 index 0000000..a2cbc19 --- /dev/null +++ b/units/ja/unit0/introduction.mdx @@ -0,0 +1,144 @@ +# 🤗 Model Context Protocol (MCP) コースへようこそ + +![MCP Course thumbnail](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/1.png) + +今日のAIにおいて最も注目すべきトピック、**Model Context Protocol (MCP)**へようこそ！ + +この無料コースは[Anthropic](https://www.anthropic.com)とのパートナーシップで構築され、MCPの理解、使用、アプリケーション構築について、**初心者から熟練者まで**の旅路にお連れします。 + +この最初のユニットでは、オンボーディングをお手伝いします： + +* **コースのシラバス**を発見する。 +* **認定プロセスとスケジュールに関する詳細情報**を取得する。 +* コース制作チームを知る。 +* **アカウント**を作成する。 +* **Discordサーバーにサインアップ**し、クラスメートや私たちと出会う。 + +始めましょう！ + +## このコースに何を期待できますか？ + +このコースでは、以下のことを行います： + +* 📖 Model Context Protocolを**理論、設計、実践**で学習する。 +* 🧑‍💻 **確立されたMCP SDKとフレームワークの使用方法**を学ぶ。 +* 💾 **プロジェクトを共有**し、コミュニティが作成したアプリケーションを探索する。 +* 🏆 **他の学生のMCP実装と自分の実装を評価**するチャレンジに参加する。 +* 🎓 課題を完了することで**修了証書を取得**する。 + +その他にも多くのことがあります！ + +このコースの最後には、**MCPがどのように動作し、最新のMCP標準を使用して外部データやツールを活用する独自のAIアプリケーションを構築する方法**を理解できるようになります。 + +[**コースにサインアップ**](https://huggingface.co/mcp-course)することをお忘れなく！ + +## コースはどのような構成ですか？ + +コースは以下で構成されています： + +* _基礎ユニット_: MCPの**概念を理論で**学ぶ部分。 +* _ハンズオン_: **確立されたMCP SDK**を使用してアプリケーションを構築することを学ぶ部分。これらのハンズオンセクションには事前設定された環境があります。 +* _ユースケース課題_: 学習した概念を適用して、あなたが選択する実世界の問題を解決する部分。 +* _コラボレーション_: Hugging Faceのパートナーと協力して、最新のMCP実装とツールを提供します。 + +この**コースは生きているプロジェクトであり、あなたのフィードバックと貢献によって進化しています！** GitHubでのissueやPRの開設、Discordサーバーでの議論への参加をお気軽にどうぞ。 + +## シラバスは何ですか？ + +以下が**コースの一般的なシラバス**です。各ユニットのリリース時により詳細なトピックリストが公開されます。 + +| チャプター | トピック | 説明 | +| ------- | ------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | +| 0 | オンボーディング | 使用するツールとプラットフォームをセットアップする。 | +| 1 | MCP基礎、アーキテクチャ、コア概念 | Model Context Protocolのコア概念、アーキテクチャ、コンポーネントを説明する。MCPを使用したシンプルなユースケースを示す。 | +| 2 | エンドツーエンドユースケース: MCP in Action | コミュニティと共有できるシンプルなエンドツーエンドMCPアプリケーションを構築する。 | +| 3 | デプロイされたユースケース: MCP in Action | Hugging Faceエコシステムとパートナーのサービスを使用してデプロイされたMCPアプリケーションを構築する。 | +| 4 | ボーナスユニット | パートナーのライブラリとサービスを使用して、コースからより多くを得るためのボーナスユニット。 | + +## 前提条件は何ですか？ + +このコースを受講するためには、以下が必要です： + +* AIとLLM概念の基本的な理解 +* ソフトウェア開発原則とAPI概念の熟知 +* 少なくとも一つのプログラミング言語の経験（PythonまたはTypeScriptの例が表示されます） + +これらのいずれかを持っていなくても心配しないでください！以下のリソースが役立ちます： + +* [LLMコース](https://huggingface.co/learn/llm-course/)では、LLMの使用と構築の基礎をガイドします。 +* [エージェントコース](https://huggingface.co/learn/agents-course/)では、LLMを使用したAIエージェントの構築をガイドします。 + + + +上記のコースは前提条件ではありませんので、LLMとエージェントの概念を理解していれば、今すぐコースを開始できます！ + + + +## どのようなツールが必要ですか？ + +必要なものは2つだけです： + +* インターネット接続のある_コンピューター_。 +* _アカウント_: コースリソースにアクセスし、プロジェクトを作成するため。まだアカウントをお持ちでない場合は、[こちら](https://huggingface.co/join)で作成できます（無料です）。 + +## 認定プロセス + +このコースを_聴講モード_で受講することも、活動を行って_私たちが発行する2つの証明書のいずれかを取得_することもできます。コースを聴講する場合、すべてのチャレンジに参加し、希望すれば課題を行うことができ、**私たちに通知する必要はありません**。 + +認定プロセスは**完全に無料**です： + +* _基礎の認定を取得する_: コースのユニット1を完了する必要があります。これは、完全なアプリケーションを構築する必要なく、MCPの最新トレンドを把握したい学生を対象としています。 +* _修了証書を取得する_: ユースケースユニット（2と3）を完了する必要があります。これは、完全なアプリケーションを構築してコミュニティと共有したい学生を対象としています。 + +## 推奨ペースは何ですか？ + +このコースの各チャプターは**1週間で完了するように設計されており、週に約3-4時間の作業時間**です。 + +期限があるため、推奨ペースを提供します： + +![Recommended Pace](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/2.png) + +## コースを最大限活用するには？ + +コースを最大限活用するために、以下のアドバイスがあります： + +1. [Discordで学習グループに参加](https://discord.gg/UrrTSsSyjb): グループで学習することは常により簡単です。そのためには、私たちのdiscordサーバーに参加し、アカウントを認証する必要があります。 +2. **クイズと課題を行う**: 学習の最良の方法は実践的な練習と自己評価です。 +3. **同期を保つためのスケジュールを定義**: 以下の推奨ペーススケジュールを使用するか、独自のものを作成できます。 + +![Course advice](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit0/3.png) + +## 私たちについて + +著者について： + +### Ben Burtenshaw + +BenはHugging FaceのMachine Learning Engineerで、ポストトレーニングとエージェンティックアプローチを使用したLLMアプリケーションの構築に焦点を当てています。[HubでBenをフォロー](https://huggingface.co/burtenshaw)して、彼の最新プロジェクトを見てください。 + +### Alex Notov + +Alexは[Anthropic](https://www.anthropic.com)のTechnical Partner Enablement Leadで、このコースのユニット3に取り組みました。AlexはAnthropicのパートナーに対して、それぞれのユースケースでのClaudeのベストプラクティスをトレーニングしています。Alexを[LinkedIn](https://linkedin.com/in/zealoushacker)と[GitHub](https://github.com/zealoushacker)でフォローしてください。 + +## 謝辞 + +貴重な貢献とサポートをいただいた以下の個人とパートナーに感謝いたします： + +- [Gradio](https://www.gradio.app/) +- [Continue](https://continue.dev) +- [Llama.cpp](https://github.com/ggerganov/llama.cpp) +- [Anthropic](https://www.anthropic.com) + +## バグを見つけた、またはコースを改善したい + +貢献は**歓迎**です 🤗 + +* _ノートブックでバグ🐛を見つけた_場合は、[issueを開いて](https://github.com/huggingface/mcp-course/issues/new)**問題を説明**してください。 +* _コースを改善したい_場合は、[Pull Requestを開く](https://github.com/huggingface/mcp-course/pulls)ことができます。 +* _完全なセクションや新しいユニットを追加したい_場合は、[issueを開いて](https://github.com/huggingface/mcp-course/issues/new)**書き始める前に追加したいコンテンツを説明**し、私たちがガイドできるようにすることが最善です。 + +## まだ質問があります + +discordサーバーの#mcp-course-questionsで質問してください。 + +すべての情報を得たので、さあ始めましょう ⛵ diff --git a/units/ja/unit1/architectural-components.mdx b/units/ja/unit1/architectural-components.mdx new file mode 100644 index 0000000..4b084c2 --- /dev/null +++ b/units/ja/unit1/architectural-components.mdx @@ -0,0 +1,85 @@ +# MCPのアーキテクチャコンポーネント + +前のセクションでは、MCPの主要概念と用語について説明しました。今度は、MCPエコシステムを構成するアーキテクチャコンポーネントについてより深く掘り下げてみましょう。 + +## ホスト、クライアント、サーバー + +Model Context Protocol（MCP）は、AIモデルと外部システム間の構造化された通信を可能にするクライアント・サーバー・アーキテクチャ上に構築されています。 + +![MCP Architecture](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/4.png) + +MCPアーキテクチャは、それぞれ明確に定義された役割と責任を持つ3つの主要コンポーネントで構成されています：ホスト、クライアント、サーバー。前のセクションでこれらについて触れましたが、各コンポーネントとその責任についてより深く掘り下げてみましょう。 + +### ホスト + +**ホスト**は、エンドユーザーが直接インタラクトするユーザー向けAIアプリケーションです。 + +例として以下があります： +- OpenAI ChatGPTやAnthropicのClaude DesktopのようなAIチャットアプリ +- CursorのようなAI強化IDE、またはContinue.devのようなツールとの統合 +- LangChainやsmolagentsのようなライブラリで構築されたカスタムAIエージェントやアプリケーション + +ホストの責任には以下が含まれます： +- ユーザーインタラクションと許可の管理 +- MCPクライアントを介したMCPサーバーへの接続開始 +- ユーザーリクエスト、LLM処理、外部ツール間の全体的なフローの統率 +- 結果を一貫した形式でユーザーに渡すレンダリング + +ほとんどの場合、ユーザーはニーズと好みに基づいてホストアプリケーションを選択します。たとえば、開発者は強力なコード編集機能を求めてCursorを選択するかもしれませんし、ドメインエキスパートはsmolagentsで構築されたカスタムアプリケーションを使用するかもしれません。 + +### クライアント + +**クライアント**は、特定のMCPサーバーとのコミュニケーションを管理するホストアプリケーション内のコンポーネントです。主要な特徴には以下が含まれます： + +- 各クライアントは単一のサーバーとの1:1接続を維持します +- MCPコミュニケーションのプロトコルレベルの詳細を処理します +- ホストのロジックと外部サーバー間の中介役として機能します + +### サーバー + +**サーバー**は、MCPプロトコルを介してAIモデルに機能を公開する外部プログラムまたはサービスです。サーバーは： + +- 特定の外部ツール、データソース、またはサービスへのアクセスを提供します +- 既存の機能の周りの軽量なラッパーとして機能します +- ローカル（ホストと同じマシン上）またはリモート（ネットワーク経由）で実行できます +- クライアントが発見し、使用できる標準化された形式で機能を公開します + +## コミュニケーションフロー + +一般的なMCPワークフローでこれらのコンポーネントがどのようにインタラクトするかを検討してみましょう： + + + +次のセクションでは、これらのコンポーネントを実用的な例で可能にするコミュニケーションプロトコルについてより深く掘り下げていきます。 + + + +1. **ユーザーインタラクション**: ユーザーが**ホスト**アプリケーションとインタラクトし、意図やクエリを表現します。 + +2. **ホスト処理**: **ホスト**がユーザーの入力を処理し、リクエストを理解し、どの外部機能が必要になる可能性があるかを決定するためにLLMを使用する可能性があります。 + +3. **クライアント接続**: **ホスト**がその**クライアント**コンポーネントに適切なサーバーに接続するよう指示します。 + +4. **機能発見**: **クライアント**が**サーバー**にクエリを送って、どのような機能（ツール、リソース、プロンプト）を提供しているかを発見します。 + +5. **機能呼び出し**: ユーザーのニーズやLLMの判断に基づいて、ホストが**クライアント**に**サーバー**から特定の機能を呼び出すよう指示します。 + +6. **サーバー実行**: **サーバー**がリクエストされた機能を実行し、結果を**クライアント**に返します。 + +7. **結果統合**: **クライアント**がこれらの結果を**ホスト**に中継し、ホストはそれらをLLMのコンテキストに組み込むか、直接ユーザーに提示します。 + +このアーキテクチャの主要な利点は、そのモジュラー性です。単一の**ホスト**が異なる**クライアント**を介して複数の**サーバー**に同時に接続できます。新しい**サーバー**を既存の**ホスト**の変更を必要とせずにエコシステムに追加できます。機能を異なる**サーバー**間で簡単に組み合わせることができます。 + + + +前のセクションで議論したように、このモジュラー性は、伝統的なM×N統合問題（M個のAIアプリケーションがN個のツール/サービスに接続）を、各ホストとサーバーがMCP標準を一度だけ実装するより管理しやすいM+N問題に変換します。 + + + +アーキテクチャはシンプルに見えるかもしれませんが、その力はコミュニケーションプロトコルの標準化とコンポーネント間の責任の明確な分離にあります。この設計により、AIモデルが常に成長し続ける外部ツールやデータソースの配列とシームレスに接続できる結束力のあるエコシステムが可能になります。 + +## 結論 + +これらのインタラクションパターンは、MCPの設計と進化を形作るいくつかの主要原則によってガイドされています。プロトコルはAI接続のための普遍的プロトコルを提供することで**標準化**を強調し、コアプロトコルを直接的に保ちながら高度な機能を可能にすることで**シンプルさ**を維持します。機密な操作に対して明示的なユーザー承認を要求することで**安全性**が優先され、発見性は機能の動的発見を可能にします。プロトコルは**拡張性**を念頭に置いて構築され、バージョニングと機能交渉を通じて進化をサポートし、異なる実装と環境間での**相互運用性**を保証します。 + +次のセクションでは、これらのコンポーネントが効果的に連携できるようにするコミュニケーションプロトコルを探求していきます。 \ No newline at end of file diff --git a/units/ja/unit1/capabilities.mdx b/units/ja/unit1/capabilities.mdx new file mode 100644 index 0000000..29f8cb4 --- /dev/null +++ b/units/ja/unit1/capabilities.mdx @@ -0,0 +1,243 @@ +# MCP機能の理解 + +MCPサーバーは、通信プロトコルを通じてクライアントに様々な機能を公開します。これらの機能は4つの主要カテゴリーに分類され、それぞれが異なる特性とユースケースを持ちます。MCPの機能の基盤を形成するこれらのコアプリミティブを探求してみましょう。 + + + +このセクションでは、各言語でフレームワーク非依存の関数として例を示します。これは、フレームワークの複雑さではなく、概念とそれらがどのように連携するかに焦点を当てるためです。 + +今後のユニットでは、これらの概念がMCP固有のコードでどのように実装されるかを示します。 + + + +## ツール + +ツールは、AIモデルがMCPプロトコルを通じて呼び出すことができる実行可能な関数またはアクションです。 + +- **制御**: ツールは通常**モデル制御**であり、AIモデル（LLM）がユーザーのリクエストとコンテキストに基づいていつ呼び出すかを決定します。 +- **安全性**: 副作用を伴うアクションを実行する能力があるため、ツールの実行は危険な場合があります。そのため、通常、明示的なユーザー承認が必要です。 +- **ユースケース**: メッセージの送信、チケットの作成、APIのクエリ、計算の実行。 + +**例**: 指定した場所の現在の天気データを取得する天気ツール： + + + + +```python +def get_weather(location: str) -> dict: + """Get the current weather for a specified location.""" + # Connect to weather API and fetch data + return { + "temperature": 72, + "conditions": "Sunny", + "humidity": 45 + } +``` + + + + +```javascript +function getWeather(location) { + // Connect to weather API and fetch data + return { + temperature: 72, + conditions: 'Sunny', + humidity: 45 + }; +} +``` + + + + +## リソース + +リソースはデータソースへの読み取り専用アクセスを提供し、AIモデルが複雑なロジックを実行することなくコンテキストを取得できるようにします。 + +- **制御**: リソースは**アプリケーション制御**であり、ホストアプリケーションが通常、いつアクセスするかを決定します。 +- **性質**: REST APIのGETエンドポイントと同様に、最小限の計算でデータ取得用に設計されています。 +- **安全性**: 読み取り専用であるため、通常ツールよりもセキュリティリスクが低くなります。 +- **ユースケース**: ファイルコンテンツのアクセス、データベースレコードの取得、設定情報の読み取り。 + +**例**: ファイルコンテンツへのアクセスを提供するリソース： + + + + +```python +def read_file(file_path: str) -> str: + """Read the contents of a file at the specified path.""" + with open(file_path, 'r') as f: + return f.read() +``` + + + + +```javascript +function readFile(filePath) { + // Using fs.readFile to read file contents + const fs = require('fs'); + return new Promise((resolve, reject) => { + fs.readFile(filePath, 'utf8', (err, data) => { + if (err) { + reject(err); + return; + } + resolve(data); + }); + }); +} +``` + + + + +## プロンプト + +プロンプトは、ユーザー、AIモデル、サーバーの機能間のインタラクションをガイドする事前定義されたテンプレートまたはワークフローです。 + +- **制御**: プロンプトは**ユーザー制御**であり、しばしばホストアプリケーションのUIでオプションとして提示されます。 +- **目的**: 利用可能なツールとリソースの最適な使用のためにインタラクションを構造化します。 +- **選択**: ユーザーは通常、AIモデルが処理を開始する前にプロンプトを選択し、インタラクションのコンテキストを設定します。 +- **ユースケース**: 一般的なワークフロー、特化タスクテンプレート、ガイド付きインタラクション。 + +**例**: コードレビューを生成するためのプロンプトテンプレート： + + + + +```python +def code_review(code: str, language: str) -> list: + """Generate a code review for the provided code snippet.""" + return [ + { + "role": "system", + "content": f"You are a code reviewer examining {language} code. Provide a detailed review highlighting best practices, potential issues, and suggestions for improvement." + }, + { + "role": "user", + "content": f"Please review this {language} code:\n\n```{language}\n{code}\n```" + } + ] +``` + + + + +```javascript +function codeReview(code, language) { + return [ + { + role: 'system', + content: `You are a code reviewer examining ${language} code. Provide a detailed review highlighting best practices, potential issues, and suggestions for improvement.` + }, + { + role: 'user', + content: `Please review this ${language} code:\n\n\`\`\`${language}\n${code}\n\`\`\`` + } + ]; +} +``` + + + + +## サンプリング + +サンプリングは、サーバーがクライアント（具体的にはホストアプリケーション）にLLMインタラクションの実行をリクエストすることを可能にします。 + +- **制御**: サンプリングは**サーバー主導**ですが、クライアント/ホストの促進が必要です。 +- **目的**: サーバー主導のエージェンティックな行動と、潜在的な再帰的またはマルチステップインタラクションを可能にします。 +- **安全性**: ツールと同様に、サンプリング操作は通常ユーザー承認が必要です。 +- **ユースケース**: 複雑なマルチステップタスク、自律エージェントワークフロー、インタラクティブプロセス。 + +**例**: サーバーがクライアントに処理したデータの分析をリクエストする場合： + + + + +```python +def request_sampling(messages, system_prompt=None, include_context="none"): + """Request LLM sampling from the client.""" + # In a real implementation, this would send a request to the client + return { + "role": "assistant", + "content": "Analysis of the provided data..." + } +``` + + + + +```javascript +function requestSampling(messages, systemPrompt = null, includeContext = 'none') { + // In a real implementation, this would send a request to the client + return { + role: 'assistant', + content: 'Analysis of the provided data...' + }; +} + +function handleSamplingRequest(request) { + const { messages, systemPrompt, includeContext } = request; + // In a real implementation, this would process the request and return a response + return { + role: 'assistant', + content: 'Response to the sampling request...' + }; +} +``` + + + + +サンプリングフローは以下のステップで進行します： +1. サーバーがクライアントに`sampling/createMessage`リクエストを送信 +2. クライアントがリクエストをレビューし、変更することができる +3. クライアントがLLMからサンプリングする +4. クライアントが完了結果をレビューする +5. クライアントが結果をサーバーに返す + + + +このヒューマン・イン・ザ・ループ設計により、ユーザーはLLMが何を見て何を生成するかを制御できます。サンプリングを実装する際は、明確で構造化されたプロンプトを提供し、関連するコンテキストを含めることが重要です。 + + + +## 機能の連携 + +これらの機能がどのように連携して複雑なインタラクションを可能にするかを見てみましょう。以下の表では、機能、それらを制御する者、制御の方向、その他の詳細を概説しています。 + +| 機能 | 制御者 | 方向 | 副作用 | 承認が必要 | 一般的なユースケース | +|------------|---------------|-----------|--------------|-----------------|-------------------| +| ツール | モデル (LLM) | クライアント → サーバー | あり（潜在的に） | はい | アクション、API呼び出し、データ操作 | +| リソース | アプリケーション | クライアント → サーバー | なし（読み取り専用） | 通常はいいえ | データ取得、コンテキスト収集 | +| プロンプト | ユーザー | サーバー → クライアント | なし | いいえ（ユーザーが選択） | ガイド付きワークフロー、特化テンプレート | +| サンプリング | サーバー | サーバー → クライアント → サーバー | 間接的に | はい | マルチステップタスク、エージェンティック行動 | + +これらの機能は相補的な方法で連携するように設計されています： + +1. ユーザーが**プロンプト**を選択して特化ワークフローを開始するかもしれません +2. プロンプトに**リソース**からのコンテキストが含まれるかもしれません +3. 処理中、AIモデルが**ツール**を呼び出して特定のアクションを実行するかもしれません +4. 複雑な操作の場合、サーバーが**サンプリング**を使用して追加のLLM処理をリクエストするかもしれません + +これらのプリミティブ間の区別は、MCPインタラクションに明確な構造を提供し、適切な制御境界を維持しながらAIモデルが情報にアクセスし、アクションを実行し、複雑なワークフローに参加できるようにします。 + +## 発見プロセス + +MCPの主要機能の一つは、動的機能発見です。クライアントがサーバーに接続すると、特定のリストメソッドを通じて利用可能なツール、リソース、プロンプトをクエリできます： + +- `tools/list`: 利用可能なツールを発見 +- `resources/list`: 利用可能なリソースを発見 +- `prompts/list`: 利用可能なプロンプトを発見 + +この動的発見メカニズムにより、クライアントはサーバーの機能に関するハードコードされた知識を必要とせずに、各サーバーが提供する特定の機能に適応できます。 + +## 結論 + +これらのコアプリミティブを理解することは、MCPを効果的に使用するために不可欠です。明確な制御境界を持つ異なるタイプの機能を提供することで、MCPは適切な安全性と制御メカニズムを維持しながら、AIモデルと外部システム間の強力なインタラクションを可能にします。 + +次のセクションでは、Gradioがこれらの機能に使いやすいインターフェースを提供するためにMCPとどのように統合されるかを探求していきます。 \ No newline at end of file diff --git a/units/ja/unit1/certificate.mdx b/units/ja/unit1/certificate.mdx new file mode 100644 index 0000000..cada2a3 --- /dev/null +++ b/units/ja/unit1/certificate.mdx @@ -0,0 +1,19 @@ +# Get your certificate! + +Well done! You've completed the first unit of the MCP course. Now it's time to take the exam to get your certificate. + +Below is a quiz to check your understanding of the unit. + + + + + +If you're struggling to use the quiz above, go to the space directly [on the Hugging Face Hub](https://huggingface.co/spaces/mcp-course/unit_1_quiz). If you find errors, you can report them in the space's [Community tab](https://huggingface.co/spaces/mcp-course/unit_1_quiz/discussions). + + + diff --git a/units/ja/unit1/communication-protocol.mdx b/units/ja/unit1/communication-protocol.mdx new file mode 100644 index 0000000..fe36d2e --- /dev/null +++ b/units/ja/unit1/communication-protocol.mdx @@ -0,0 +1,223 @@ +# 通信プロトコル + +MCPは、クライアントとサーバーが一貫性があり予測可能な方法でメッセージを交換できるようにする標準化された通信プロトコルを定義しています。この標準化は、コミュニティ全体での相互運用性にとって重要です。このセクションでは、MCPで使用されるプロトコル構造と転送メカニズムを探求します。 + + + +MCPプロトコルの詳細について説明していきます。MCPを使って構築するためにすべてを知る必要はありませんが、それが存在し、どのように機能するかを知っておくことは良いことです。 + + + +## JSON-RPC: 基盤 + +コアにおいて、MCPはクライアントとサーバー間のすべてのコミュニケーションのメッセージ形式として**JSON-RPC 2.0**を使用します。JSON-RPCは、JSONでエンコードされた軽量のリモートプロシージャー呼び出しプロトコルであり、以下の特徴を持ちます： + +- 人間が読みやすく、デバッグが容易 +- 言語非依存で、あらゆるプログラミング環境での実装をサポート +- 確立された技術であり、明確な仕様と幅広い採用を持つ + +![message types](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/5.png) + +プロトコルは3種類のメッセージを定義します： + +### 1. リクエスト + +操作を開始するためにクライアントからサーバーに送信されます。リクエストメッセージには以下が含まれます： +- 一意の識別子（`id`） +- 呼び出すメソッド名（例：`tools/call`） +- メソッドのパラメーター（ある場合） + +リクエストの例： + +```json +{ + "jsonrpc": "2.0", + "id": 1, + "method": "tools/call", + "params": { + "name": "weather", + "arguments": { + "location": "San Francisco" + } + } +} +``` + +### 2. レスポンス + +リクエストに対する返信としてサーバーからクライアントに送信されます。レスポンスメッセージには以下が含まれます： +- 対応するリクエストと同じ`id` +- `result`（成功の場合）または`error`（失敗の場合）のいずれか + +成功レスポンスの例： +```json +{ + "jsonrpc": "2.0", + "id": 1, + "result": { + "temperature": 62, + "conditions": "Partly cloudy" + } +} +``` + +エラーレスポンスの例： +```json +{ + "jsonrpc": "2.0", + "id": 1, + "error": { + "code": -32602, + "message": "Invalid location parameter" + } +} +``` + +### 3. 通知 + +レスポンスを必要としない一方向メッセージ。通常、イベントに関する更新や通知を提供するためにサーバーからクライアントに送信されます。 + +通知の例： +```json +{ + "jsonrpc": "2.0", + "method": "progress", + "params": { + "message": "Processing data...", + "percent": 50 + } +} +``` + +## 転送メカニズム + +JSON-RPCはメッセージ形式を定義しますが、MCPはこれらのメッセージがクライアントとサーバー間でどのように転送されるかも指定します。2つの主要な転送メカニズムがサポートされています： + +### stdio（標準入力/出力） + +stdio転送は、クライアントとサーバーが同じマシンで実行されるローカル通信に使用されます： + +ホストアプリケーションはサーバーをサブプロセスとして起動し、その標準入力（stdin）に書き込み、標準出力（stdout）から読み取ることでコミュニケーションを行います。 + + + +この転送の**ユースケース**は、ファイルシステムアクセスやローカルスクリプトの実行のようなローカルツールです。 + + + +この転送の主要な**利点**は、シンプルで、ネットワーク設定が不要で、オペレーティングシステムによって安全にサンドボックス化されることです。 + +### HTTP + SSE（Server-Sent Events）/ Streamable HTTP + +HTTP+SSE転送は、クライアントとサーバーが異なるマシンにある可能性があるリモート通信に使用されます： + +コミュニケーションはHTTPで行われ、サーバーはServer-Sent Events（SSE）を使用して永続接続でクライアントに更新をプッシュします。 + + + +この転送の**ユースケース**は、リモートAPI、クラウドサービス、または共有リソースへの接続です。 + + + +この転送の主要な**利点**は、ネットワークで機能し、Webサービスとの統合を可能にし、サーバーレス環境と互換性があることです。 + +MCP標準の最近の更新では、「Streamable HTTP」が導入または改善され、サーバーレス環境との互換性を維持しながら、必要に応じてサーバーがストリーミング用にSSEに動的にアップグレードできるようにすることで、より柔軟性を提供します。 + +## インタラクションライフサイクル + +前のセクションでは、クライアント（💻）とサーバー（🌐）間の単一インタラクションのライフサイクルについて議論しました。今度は、MCPプロトコルのコンテキストでクライアントとサーバー間の完全なインタラクションのライフサイクルを見てみましょう。 + +MCPプロトコルは、クライアントとサーバー間の構造化されたインタラクションライフサイクルを定義します： + +### 初期化 + +クライアントがサーバーに接続し、プロトコルバージョンと機能を交換し、サーバーはサポートするプロトコルバージョンと機能で応答します。 + + + + + + + + + + + + + + + + + +
💻→
initialize		🌐
💻←
response		🌐
💻→
initialized		🌐
+ +クライアントは通知メッセージで初期化が完了したことを確認します。 + +### 発見 + +クライアントが利用可能な機能に関する情報をリクエストし、サーバーが利用可能なツールのリストで応答します。 + + + + + + + + + + + + +
💻→
tools/list		🌐
💻←
response		🌐
+ +このプロセスは、各ツール、リソース、またはプロンプトタイプに対して繰り返される可能性があります。 + +### 実行 + +クライアントがホストのニーズに基づいて機能を呼び出します。 + + + + + + + + + + + + + + + + + +
💻→
tools/call		🌐
💻←
notification (オプションの進行状況)		🌐
💻←
response		🌐
+ +### 終了 + +必要がなくなったときに接続が適切にクローズされ、サーバーがシャットダウンリクエストを確認します。 + + + + + + + + + + + + + + + + + +
💻→
shutdown		🌐
💻←
response		🌐
💻→
exit		🌐
+ +クライアントは終了を完了するために最終的なexitメッセージを送信します。 + +## プロトコルの進化 + +MCPプロトコルは、拡張可能で適応性があるように設計されています。初期化フェーズにはバージョン交渉が含まれ、プロトコルの進化に伴って後方互換性を可能にします。さらに、機能発見によりクライアントが各サーバーが提供する特定の機能に適応できるようになり、同じエコシステム内で基本的なサーバーと高度なサーバーの混在を可能にします。 diff --git a/units/ja/unit1/gradio-mcp.mdx b/units/ja/unit1/gradio-mcp.mdx new file mode 100644 index 0000000..8c87bff --- /dev/null +++ b/units/ja/unit1/gradio-mcp.mdx @@ -0,0 +1,154 @@ +# Gradio MCP Integration + +We've now explored the core concepts of the MCP protocol and how to implement MCP Servers and Clients. In this section, we're going to make things slightly easier by using Gradio to create an MCP Server! + + + +Gradio is a popular Python library for quickly creating customizable web interfaces for machine learning models. + + + +## Introduction to Gradio + +Gradio allows developers to create UIs for their models with just a few lines of Python code. It's particularly useful for: + +- Creating demos and prototypes +- Sharing models with non-technical users +- Testing and debugging model behavior + +With the addition of MCP support, Gradio now offers a straightforward way to expose AI model capabilities through the standardized MCP protocol. + +Combining Gradio with MCP allows you to create both human-friendly interfaces and AI-accessible tools with minimal code. But best of all, Gradio is already well-used by the AI community, so you can use it to share your MCP Servers with others. + +## Prerequisites + +To use Gradio with MCP support, you'll need to install Gradio with the MCP extra: + +```bash +pip install "gradio[mcp]" +``` + +You'll also need an LLM application that supports tool calling using the MCP protocol, such as Cursor ( known as "MCP Hosts"). + +## Creating an MCP Server with Gradio + +Let's walk through a basic example of creating an MCP Server using Gradio: + +```python +import gradio as gr + +def letter_counter(word: str, letter: str) -> int: + """ + Count the number of occurrences of a letter in a word or text. + + Args: + word (str): The input text to search through + letter (str): The letter to search for + + Returns: + int: The number of times the letter appears in the text + """ + word = word.lower() + letter = letter.lower() + count = word.count(letter) + return count + +# Create a standard Gradio interface +demo = gr.Interface( + fn=letter_counter, + inputs=["textbox", "textbox"], + outputs="number", + title="Letter Counter", + description="Enter text and a letter to count how many times the letter appears in the text." +) + +# Launch both the Gradio web interface and the MCP server +if __name__ == "__main__": + demo.launch(mcp_server=True) +``` + +With this setup, your letter counter function is now accessible through: + +1. A traditional Gradio web interface for direct human interaction +2. An MCP Server that can be connected to compatible clients + +The MCP server will be accessible at: +``` +http://your-server:port/gradio_api/mcp/sse +``` + +The application itself will still be accessible and it looks like this: + +![Gradio MCP Server](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/7.png) + +## How It Works Behind the Scenes + +When you set `mcp_server=True` in `launch()`, several things happen: + +1. Gradio functions are automatically converted to MCP Tools +2. Input components map to tool argument schemas +3. Output components determine the response format +4. The Gradio server now also listens for MCP protocol messages +5. JSON-RPC over HTTP+SSE is set up for client-server communication + +## Key Features of the Gradio <> MCP Integration + +1. **Tool Conversion**: Each API endpoint in your Gradio app is automatically converted into an MCP tool with a corresponding name, description, and input schema. To view the tools and schemas, visit `http://your-server:port/gradio_api/mcp/schema` or go to the "View API" link in the footer of your Gradio app, and then click on "MCP". + +2. **Environment Variable Support**: There are two ways to enable the MCP server functionality: +- Using the `mcp_server` parameter in `launch()`: + ```python + demo.launch(mcp_server=True) + ``` +- Using environment variables: + ```bash + export GRADIO_MCP_SERVER=True + ``` + +3. **File Handling**: The server automatically handles file data conversions, including: + - Converting base64-encoded strings to file data + - Processing image files and returning them in the correct format + - Managing temporary file storage + + It is **strongly** recommended that input images and files be passed as full URLs ("http://..." or "https://...") as MCP Clients do not always handle local files correctly. + +4. **Hosted MCP Servers on 🤗 Spaces**: You can publish your Gradio application for free on Hugging Face Spaces, which will allow you to have a free hosted MCP server. Here's an example of such a Space: https://huggingface.co/spaces/abidlabs/mcp-tools + +## Troubleshooting Tips + +1. **Type Hints and Docstrings**: Ensure you provide type hints and valid docstrings for your functions. The docstring should include an "Args:" block with indented parameter names. + +2. **String Inputs**: When in doubt, accept input arguments as `str` and convert them to the desired type inside the function. + +3. **SSE Support**: Some MCP Hosts don't support SSE-based MCP Servers. In those cases, you can use `mcp-remote`: + ```json + { + "mcpServers": { + "gradio": { + "command": "npx", + "args": [ + "mcp-remote", + "http://your-server:port/gradio_api/mcp/sse" + ] + } + } + } + ``` + +4. **Restart**: If you encounter connection issues, try restarting both your MCP Client and MCP Server. + +## Share your MCP Server + +You can share your MCP Server by publishing your Gradio app to Hugging Face Spaces. The video below shows how to create a Hugging Face Space. + + + +Now, you can share your MCP Server with others by sharing your Hugging Face Space. + +## Conclusion + +Gradio's integration with MCP provides an accessible entry point to the MCP ecosystem. By leveraging Gradio's simplicity and adding MCP's standardization, developers can quickly create both human-friendly interfaces and AI-accessible tools with minimal code. + +As we progress through this course, we'll explore more sophisticated MCP implementations, but Gradio offers an excellent starting point for understanding and experimenting with the protocol. + +In the next unit, we'll dive deeper into building MCP applications, focusing on setting up development environments, exploring SDKs, and implementing more advanced MCP Servers and Clients. diff --git a/units/ja/unit1/introduction.mdx b/units/ja/unit1/introduction.mdx new file mode 100644 index 0000000..212bae3 --- /dev/null +++ b/units/ja/unit1/introduction.mdx @@ -0,0 +1,33 @@ +# Model Context Protocol (MCP)の紹介 + +MCPコースのユニット1へようこそ！このユニットでは、Model Context Protocolの基礎を探求していきます。 + +## 学習内容 + +このユニットでは、以下のことを学習します： + +* Model Context Protocolとは何か、そしてなぜ重要なのかを理解する +* MCPに関連する主要概念と用語を学ぶ +* MCPが解決する統合課題を探求する +* MCPの主要な利点と目標を理解する +* MCP統合の実際の動作例を見る + +このユニットの終了時には、MCPの基礎概念をしっかりと理解し、次のユニットでアーキテクチャと実装についてより深く学ぶ準備が整うでしょう。 + +## MCPの重要性 + +AIエコシステムは急速に進化しており、大規模言語モデル（LLM）や他のAIシステムがますます高性能になっています。しかし、これらのモデルは多くの場合、学習データに制限され、リアルタイム情報や専門ツールへのアクセスが不足しています。この制限により、多くのシナリオでAIシステムが真に関連性があり、正確で役立つ応答を提供する可能性が妨げられています。 + +ここでModel Context Protocol（MCP）が登場します。MCPは、AIモデルが外部データソース、ツール、環境と接続することを可能にし、AIシステムと広範なデジタル世界の間で情報と機能のシームレスな転送を可能にします。この相互運用性は、真に有用なAIアプリケーションの成長と採用にとって重要です。 + +## ユニット1の概要 + +このユニットで取り扱う内容の簡単な概要は以下の通りです： + +1. **Model Context Protocolとは何か？** - MCPが何であり、AIエコシステムにおけるその役割について定義し、議論することから始めます。 +2. **主要概念** - MCPに関連する基本概念と用語を探求します。 +3. **統合課題** - MCPが解決を目指す問題、特に「M×N統合問題」を検討します。 +4. **利点と目標** - 標準化、強化されたAI機能、相互運用性を含む、MCPの主要な利点と目標について議論します。 +5. **シンプルな例** - 最後に、MCP統合のシンプルな例を見て、実際にどのように動作するかを確認します。 + +Model Context Protocolのエキサイティングな世界を探求してみましょう！ \ No newline at end of file diff --git a/units/ja/unit1/key-concepts.mdx b/units/ja/unit1/key-concepts.mdx new file mode 100644 index 0000000..6058b76 --- /dev/null +++ b/units/ja/unit1/key-concepts.mdx @@ -0,0 +1,92 @@ +# 主要概念と用語 + +Model Context Protocolについてより深く掘り下げる前に、MCPの基盤を形成する主要概念と用語を理解することが重要です。このセクションでは、プロトコルを支える基本的なアイデアを紹介し、コース全体を通じてMCP実装について議論するための共通語彙を提供します。 + +MCPは「AIアプリケーション向けのUSB-C」としてよく説明されます。USB-Cが様々な周辺機器をコンピューティングデバイスに接続するための標準化された物理的および論理的インターフェースを提供するのと同様に、MCPはAIモデルを外部機能にリンクするための一貫したプロトコルを提供します。この標準化はエコシステム全体に利益をもたらします： + +- **ユーザー** はAIアプリケーション全体でよりシンプルで一貫した体験を享受できます +- **AIアプリケーション開発者** は成長するツールとデータソースのエコシステムとの簡単な統合を得られます +- **ツールとデータプロバイダー** は複数のAIアプリケーションで動作する単一の実装を作成するだけで済みます +- より広範なエコシステムは相互運用性の向上、イノベーション、分断化の削減から恩恵を受けます + +## 統合問題 + +**M×N統合問題**とは、標準化されたアプローチなしにM個の異なるAIアプリケーションをN個の異なる外部ツールやデータソースに接続する際の課題を指します。 + +### MCPなしの場合（M×N問題） + +MCPのようなプロトコルがなければ、開発者はM×N個のカスタム統合を作成する必要があります—AIアプリケーションと外部機能の可能な組み合わせごとに1つずつ。 + +![Without MCP](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/1.png) + +各AIアプリケーションは、各ツール/データソースと個別に統合する必要があります。これは非常に複雑で高コストなプロセスであり、開発者に多くの摩擦をもたらし、高いメンテナンスコストが発生します。 + +複数のモデルと複数のツールがある場合、統合の数は管理するには大きすぎ、それぞれが独自のユニークなインターフェースを持ちます。 + +![Multiple Models and Tools](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/1a.png) + +### MCPありの場合（M+Nソリューション） + +MCPは標準インターフェースを提供することで、これをM+N問題に変換します：各AIアプリケーションはMCPのクライアント側を一度実装し、各ツール/データソースはサーバー側を一度実装します。これにより統合の複雑性とメンテナンス負荷が劇的に削減されます。 + +![With MCP](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/2.png) + +各AIアプリケーションはMCPのクライアント側を一度実装し、各ツール/データソースはサーバー側を一度実装します。 + +## コアMCP用語 + +MCPが解決する問題を理解した今、MCPプロトコルを構成するコア用語と概念に深く入りましょう。 + + + +MCPはHTTPやUSB-Cのような標準であり、AIアプリケーションを外部ツールやデータソースに接続するためのプロトコルです。そのため、標準用語を使用することは、MCPを効果的に機能させるために重要です。 + +アプリケーションを文書化し、コミュニティとコミュニケーションを取る際には、以下の用語を使用すべきです。 + + + +### コンポーネント + +HTTPのクライアントサーバー関係と同じように、MCPにはクライアントとサーバーがあります。 + +![MCP Components](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/3.png) + +- **ホスト**: エンドユーザーが直接インタラクトするユーザー向けAIアプリケーション。例としては、AnthropicのClaude Desktop、CursorのようなAI強化IDE、Hugging Face Python SDKのような推論ライブラリ、またはLangChainやsmolagentsのようなライブラリで構築されたカスタムアプリケーションがあります。ホストはMCPサーバーへの接続を開始し、ユーザーリクエスト、LLM処理、外部ツール間の全体的なフローを統率します。 + +- **クライアント**: 特定のMCPサーバーとのコミュニケーションを管理するホストアプリケーション内のコンポーネント。各クライアントは単一のサーバーとの1:1接続を維持し、MCPコミュニケーションのプロトコルレベルの詳細を処理し、ホストのロジックと外部サーバー間の中介役として機能します。 + +- **サーバー**: MCPプロトコルを介して機能（ツール、リソース、プロンプト）を公開する外部プログラムまたはサービス。 + + + +多くのコンテンツでは「クライアント」と「ホスト」が交換可能に使用されています。技術的に言えば、ホストはユーザー向けアプリケーションであり、クライアントは特定のMCPサーバーとのコミュニケーションを管理するホストアプリケーション内のコンポーネントです。 + + + +### 機能 + +もちろん、アプリケーションの価値は、それが提供する機能の総和です。したがって、機能はアプリケーションの最も重要な部分です。MCPはあらゆるソフトウェアサービスと接続できますが、多くのAIアプリケーションで使用される一般的な機能がいくつかあります。 + +| 機能 | 説明 | 例 | +| ---------- | ----------- | ------- | +| **ツール** | AIモデルがアクションを実行したり、計算されたデータを取得するために呼び出すことができる実行可能な関数。通常、アプリケーションのユースケースに関連している。 | 天気アプリケーションのツールは、特定の場所の天気を返す関数である可能性があります。 | +| **リソース** | 重要な計算を伴わずにコンテキストを提供する読み取り専用データソース。 | 研究アシスタントは科学論文のリソースを持っている可能性があります。 | +| **プロンプト** | ユーザー、AIモデル、利用可能な機能間のインタラクションをガイドする事前定義されたテンプレートまたはワークフロー。 | 要約プロンプト。 | +| **サンプリング** | クライアント/ホストにLLMインタラクションを実行させるサーバー開始のリクエストで、LLMが生成されたコンテンツをレビューし、さらなる決定を行うことを可能にする再帰的アクションを実現します。 | ライティングアプリケーションが自分の出力をレビューし、さらに精巧にすることを決定する。 | + +以下の図では、コードエージェントのユースケースに適用された集合的機能を見ることができます。 + +![collective diagram](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/8.png) + +このアプリケーションは、MCPエンティティを以下のように使用する可能性があります： + +| エンティティ | 名前 | 説明 | +| --- | --- | --- | +| ツール | コードインタープリター | LLMが書いたコードを実行できるツール。 | +| リソース | ドキュメンテーション | アプリケーションのドキュメンテーションを含むリソース。 | +| プロンプト | コードスタイル | LLMにコードを生成するようガイドするプロンプト。 | +| サンプリング | コードレビュー | LLMがコードをレビューし、さらなる決定を行うことを可能にするサンプリング。 | + +### 結論 + +これらの主要概念と用語を理解することは、MCPを効果的に使用するための基盤を提供します。以下のセクションでは、この基盤をベースに、Model Context Protocolを構成するアーキテクチャコンポーネント、コミュニケーションプロトコル、機能を探求していきます。 diff --git a/units/ja/unit1/mcp-clients.mdx b/units/ja/unit1/mcp-clients.mdx new file mode 100644 index 0000000..63f77ad --- /dev/null +++ b/units/ja/unit1/mcp-clients.mdx @@ -0,0 +1,357 @@ +# MCP Clients + +Now that we have a basic understanding of the Model Context Protocol, we can explore the essential role of MCP Clients in the Model Context Protocol ecosystem. + + In this part of Unit 1, we'll explore the essential role of MCP Clients in the Model Context Protocol ecosystem. + +In this section, you will: + +* Understand what MCP Clients are and their role in the MCP architecture +* Learn about the key responsibilities of MCP Clients +* Explore the major MCP Client implementations +* Discover how to use Hugging Face's MCP Client implementation +* See practical examples of MCP Client usage + + + +In this page we're going to show examples of how to set up MCP Clients in a few different ways using the JSON notation. For now, we will use *examples* like `path/to/server.py` to represent the path to the MCP Server. In the next unit, we'll implement this with real MCP Servers. + +For now, focus on understanding the MCP Client notation. We'll implement the MCP Servers in the next unit. + + + +## Understanding MCP Clients + +MCP Clients are crucial components that act as the bridge between AI applications (Hosts) and external capabilities provided by MCP Servers. Think of the Host as your main application (like an AI assistant or IDE) and the Client as a specialized module within that Host responsible for handling MCP communications. + +## User Interface Client + +Let's start by exploring the user interface clients that are available for the MCP. + +### Chat Interface Clients + +Anthropic's Claude Desktop stands as one of the most prominent MCP Clients, providing integration with various MCP Servers. + +### Interactive Development Clients + +Cursor's MCP Client implementation enables AI-powered coding assistance through direct integration with code editing capabilities. It supports multiple MCP Server connections and provides real-time tool invocation during coding, making it a powerful tool for developers. + +Continue.dev is another example of an interactive development client that supports MCP and connects to an MCP server from VS Code. + +## Configuring MCP Clients + +Now that we've covered the core of the MCP protocol, let's look at how to configure your MCP servers and clients. + +Effective deployment of MCP servers and clients requires proper configuration. + + + +The MCP specification is still evolving, so the configuration methods are subject to evolution. We'll focus on the current best practices for configuration. + + + +### MCP Configuration Files + +MCP hosts use configuration files to manage server connections. These files define which servers are available and how to connect to them. + +Fortunately, the configuration files are very simple, easy to understand, and consistent across major MCP hosts. + +#### `mcp.json` Structure + +The standard configuration file for MCP is named `mcp.json`. Here's the basic structure: + +This is the basic structure of the `mcp.json` can be passed to applications like Claude Desktop, Cursor, or VS Code. + +```json +{ + "servers": [ + { + "name": "Server Name", + "transport": { + "type": "stdio|sse", + // Transport-specific configuration + } + } + ] +} +``` + +In this example, we have a single server with a name and a transport type. The transport type is either `stdio` or `sse`. + +#### Configuration for stdio Transport + +For local servers using stdio transport, the configuration includes the command and arguments to launch the server process: + +```json +{ + "servers": [ + { + "name": "File Explorer", + "transport": { + "type": "stdio", + "command": "python", + "args": ["/path/to/file_explorer_server.py"] // This is an example, we'll use a real server in the next unit + } + } + ] +} +``` + +Here, we have a server called "File Explorer" that is a local script. + +#### Configuration for HTTP+SSE Transport + +For remote servers using HTTP+SSE transport, the configuration includes the server URL: + +```json +{ + "servers": [ + { + "name": "Remote API Server", + "transport": { + "type": "sse", + "url": "https://example.com/mcp-server" + } + } + ] +} +``` + +#### Environment Variables in Configuration + +Environment variables can be passed to server processes using the `env` field. Here's how to access them in your server code: + + + + +In Python, we use the `os` module to access environment variables: + +```python +import os + +# Access environment variables +github_token = os.environ.get("GITHUB_TOKEN") +if not github_token: + raise ValueError("GITHUB_TOKEN environment variable is required") + +# Use the token in your server code +def make_github_request(): + headers = {"Authorization": f"Bearer {github_token}"} + # ... rest of your code +``` + + + + +In JavaScript, we use the `process.env` object to access environment variables: + +```javascript +// Access environment variables +const githubToken = process.env.GITHUB_TOKEN; +if (!githubToken) { + throw new Error("GITHUB_TOKEN environment variable is required"); +} + +// Use the token in your server code +function makeGithubRequest() { + const headers = { "Authorization": `Bearer ${githubToken}` }; + // ... rest of your code +} +``` + + + + +The corresponding configuration in `mcp.json` would look like this: + +```json +{ + "servers": [ + { + "name": "GitHub API", + "transport": { + "type": "stdio", + "command": "python", + "args": ["/path/to/github_server.py"], // This is an example, we'll use a real server in the next unit + "env": { + "GITHUB_TOKEN": "your_github_token" + } + } + } + ] +} +``` + +### Configuration Examples + +Let's look at some real-world configuration scenarios: + +#### Scenario 1: Local Server Configuration + +In this scenario, we have a local server that is a Python script which could be a file explorer or a code editor. + +```json +{ + "servers": [ + { + "name": "File Explorer", + "transport": { + "type": "stdio", + "command": "python", + "args": ["/path/to/file_explorer_server.py"] // This is an example, we'll use a real server in the next unit + } + } + ] +} +``` + +#### Scenario 2: Remote Server Configuration + +In this scenario, we have a remote server that is a weather API. + +```json +{ + "servers": [ + { + "name": "Weather API", + "transport": { + "type": "sse", + "url": "https://example.com/mcp-server" // This is an example, we'll use a real server in the next unit + } + } + ] +} +``` + +Proper configuration is essential for successfully deploying MCP integrations. By understanding these aspects, you can create robust and reliable connections between AI applications and external capabilities. + +In the next section, we'll explore the ecosystem of MCP servers available on Hugging Face Hub and how to publish your own servers there. + +## Tiny Agents Clients + +Now, let's explore how to use MCP Clients within code. + +You can also use tiny agents as MCP Clients to connect directly to MCP servers from your code. Tiny agents provide a simple way to create AI agents that can use tools from MCP servers. + +Tiny Agent can run MCP servers with a command line environment. To do this, we will need to install `npm` and run the server with `npx`. **We'll need these for both Python and JavaScript.** + +Let's install `npx` with `npm`. If you don't have `npm` installed, check out the [npm documentation](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm). + +### Setup + +First, we will need to install `npx` if you don't have it installed. You can do this with the following command: + +```bash +# install npx +npm install -g npx +``` + +Then, we will need to install the huggingface_hub package with the MCP support. This will allow us to run MCP servers and clients. + +```bash +pip install "huggingface_hub[mcp]>=0.32.0" +``` + +Then, we will need to log in to the Hugging Face Hub to access the MCP servers. You can do this with the `huggingface-cli` command line tool. You will need a [login token](https://huggingface.co/docs/huggingface_hub/v0.32.3/en/quick-start#authentication) to do this. + +```bash +huggingface-cli login +``` + + + + +### Connecting to MCP Servers + +Now, let's create an agent configuration file `agent.json`. + +```json +{ + "model": "Qwen/Qwen2.5-72B-Instruct", + "provider": "nebius", + "servers": [ + { + "type": "stdio", + "config": { + "command": "npx", + "args": ["@playwright/mcp@latest"] + } + } + ] +} +``` + +In this configuration, we are using the `@playwright/mcp` MCP server. This is an MCP server that can control a browser with Playwright. + +Now you can run the agent: + +```bash +tiny-agents run agent.json +``` + + + +First, install the tiny agents package with [npm](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm). + +```bash +npm install @huggingface/tiny-agents +``` + +### Connecting to MCP Servers + +Make an agent project directory and create an `agent.json` file. + +```bash +mkdir my-agent +touch my-agent/agent.json +``` + +Create an agent configuration file at `my-agent/agent.json`: + +```json +{ + "model": "Qwen/Qwen2.5-72B-Instruct", + "provider": "nebius", + "servers": [ + { + "type": "stdio", + "config": { + "command": "npx", + "args": ["@playwright/mcp@latest"] + } + } + ] +} +``` + +Now you can run the agent: + +```bash +npx @huggingface/tiny-agents run ./my-agent +``` + + + + +In the video below, we run the agent and ask it to open a new tab in the browser. + +The following example shows a web-browsing agent configured to use the [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) model via Nebius inference provider, and it comes equipped with a playwright MCP server, which lets it use a web browser! The agent config is loaded specifying [its path in the `tiny-agents/tiny-agents`](https://huggingface.co/datasets/tiny-agents/tiny-agents/tree/main/celinah/web-browser) Hugging Face dataset. + + + +When you run the agent, you'll see it load, listing the tools it has discovered from its connected MCP servers. Then, it's ready for your prompts! + +Prompt used in this demo: + +> do a Web Search for HF inference providers on Brave Search and open the first result and then give me the list of the inference providers supported on Hugging Face + +## Next Steps + +Now that you understand MCP Clients, you're ready to: +* Explore specific MCP Server implementations +* Learn about creating custom MCP Clients +* Dive into advanced MCP integration patterns + +Let's continue our journey into the world of Model Context Protocol! diff --git a/units/ja/unit1/quiz1.mdx b/units/ja/unit1/quiz1.mdx new file mode 100644 index 0000000..fda8b9b --- /dev/null +++ b/units/ja/unit1/quiz1.mdx @@ -0,0 +1,125 @@ +# Quiz 1: MCP Fundamentals + +Test your knowledge of the core concepts of Model Context Protocol. + +### Q1: What is the primary purpose of Model Context Protocol (MCP)? + + + +### Q2: What problem does MCP primarily aim to solve? + + + +### Q3: Which of the following is a key benefit of MCP? + + + +### Q4: In MCP terminology, what is a "Host"? + + + +### Q5: What does "M×N Integration Problem" refer to in the context of AI applications? + + + +Congrats on finishing this Quiz 🥳! If you need to review any elements, take the time to revisit the chapter to reinforce your knowledge. \ No newline at end of file diff --git a/units/ja/unit1/quiz2.mdx b/units/ja/unit1/quiz2.mdx new file mode 100644 index 0000000..a31ee7f --- /dev/null +++ b/units/ja/unit1/quiz2.mdx @@ -0,0 +1,125 @@ +# Quiz 2: MCP SDK + +Test your knowledge of the MCP SDKs and their functionalities. + +### Q1: What is the main purpose of the MCP SDKs? + + + +### Q2: Which of the following functionalities do the MCP SDKs typically handle? + + + +### Q3: According to the provided text, which company maintains the official Python SDK for MCP? + + + +### Q4: What command is used to start a development MCP server using a Python file named `server.py`? + + + +### Q5: What is the role of JSON-RPC 2.0 in MCP? + + + +Congrats on finishing this Quiz 🥳! If you need to review any elements, take the time to revisit the chapter to reinforce your knowledge. \ No newline at end of file diff --git a/units/ja/unit1/sdk.mdx b/units/ja/unit1/sdk.mdx new file mode 100644 index 0000000..765c06f --- /dev/null +++ b/units/ja/unit1/sdk.mdx @@ -0,0 +1,179 @@ +# MCP SDK + +Model Context Protocolは、JavaScript、Python、その他の言語向けの公式SDKを提供しています。これにより、アプリケーションでMCPクライアントとサーバーを簡単に実装できます。これらのSDKは低レベルのプロトコル詳細を処理し、アプリケーションの機能構築に集中できるようにします。 + +## SDKの概要 + +両SDKは、先ほど説明したMCPプロトコル仕様に従って、同様のコア機能を提供します。これらは以下を処理します： + +- プロトコルレベルの通信 +- 機能の登録と発見 +- メッセージのシリアライゼーション/デシリアライゼーション +- 接続管理 +- エラーハンドリング + +## コアプリミティブの実装 + +両SDKを使用してコアプリミティブ（ツール、リソース、プロンプト）のそれぞれを実装する方法を探求してみましょう。 + + + + + + +```python +from mcp.server.fastmcp import FastMCP + +# Create an MCP server +mcp = FastMCP("Weather Service") + +# Tool implementation +@mcp.tool() +def get_weather(location: str) -> str: + """Get the current weather for a specified location.""" + return f"Weather in {location}: Sunny, 72°F" + +# Resource implementation +@mcp.resource("weather://{location}") +def weather_resource(location: str) -> str: + """Provide weather data as a resource.""" + return f"Weather data for {location}: Sunny, 72°F" + +# Prompt implementation +@mcp.prompt() +def weather_report(location: str) -> str: + """Create a weather report prompt.""" + return f"""You are a weather reporter. Weather report for {location}?""" + + +# Run the server +if __name__ == "__main__": + mcp.run() +``` + +サーバーを実装したら、サーバースクリプトを実行して起動できます。 + +```bash +mcp dev server.py +``` + + + + +```javascript +// index.mjs +import { + McpServer, + ResourceTemplate, +} from "@modelcontextprotocol/sdk/server/mcp.js"; +import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; +import { z } from "zod"; + +// Create an MCP server +const server = new McpServer({ + name: "Weather Service", + version: "1.0.0", +}); + +// Tool implementation +server.tool("get_weather", { location: z.string() }, async ({ location }) => ({ + content: [ + { + type: "text", + text: `Weather in ${location}: Sunny, 72°F`, + }, + ], +})); + +// Resource implementation +server.resource( + "weather", + new ResourceTemplate("weather://{location}", { list: undefined }), + async (uri, { location }) => ({ + contents: [ + { + uri: uri.href, + text: `Weather data for ${location}: Sunny, 72°F`, + }, + ], + }) +); + +// Prompt implementation +server.prompt( + "weather_report", + { location: z.string() }, + async ({ location }) => ({ + messages: [ + { + role: "assistant", + content: { + type: "text", + text: "You are a weather reporter.", + }, + }, + { + role: "user", + content: { + type: "text", + text: `Weather report for ${location}?`, + }, + }, + ], + }) +); + +// Run the server +const transport = new StdioServerTransport(); +await server.connect(transport); +``` + +サーバーを実装したら、サーバースクリプトを実行して起動できます。 + +```bash +npx @modelcontextprotocol/inspector node ./index.mjs +``` + + + + +これにより、ファイル`server.py`を実行する開発サーバーが初期化され、以下の出力がログに表示されます： + +```bash +Starting MCP inspector... +⚙️ Proxy server listening on port 6277 +Spawned stdio transport +Connected MCP client to backing server transport +Created web app transport +Set up MCP proxy +🔍 MCP Inspector is up and running at http://127.0.0.1:6274 🚀 +``` + +その後、[http://127.0.0.1:6274](http://127.0.0.1:6274)でMCP Inspectorを開いて、サーバーの機能を見て、それらとインタラクトできます。 + +UIを通じてサーバーの機能とそれらを呼び出す機能が表示されます。 + +![MCP Inspector](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit1/6.png) + +## MCP SDK + +MCPは言語非依存に設計されており、いくつかの人気のあるプログラミング言語向けの公式SDKが利用可能です： + +| 言語 | リポジトリ | メンテナー | ステータス | +| ---------- | -------------------------------------------------------------------------------------------------------- | ------------------- | ---------------- | +| TypeScript | [github.com/modelcontextprotocol/typescript-sdk](https://github.com/modelcontextprotocol/typescript-sdk) | Anthropic | アクティブ | +| Python | [github.com/modelcontextprotocol/python-sdk](https://github.com/modelcontextprotocol/python-sdk) | Anthropic | アクティブ | +| Java | [github.com/modelcontextprotocol/java-sdk](https://github.com/modelcontextprotocol/java-sdk) | Spring AI (VMware) | アクティブ | +| Kotlin | [github.com/modelcontextprotocol/kotlin-sdk](https://github.com/modelcontextprotocol/kotlin-sdk) | JetBrains | アクティブ | +| C# | [github.com/modelcontextprotocol/csharp-sdk](https://github.com/modelcontextprotocol/csharp-sdk) | Microsoft | アクティブ (プレビュー) | +| Swift | [github.com/modelcontextprotocol/swift-sdk](https://github.com/modelcontextprotocol/swift-sdk) | loopwork-ai | アクティブ | +| Rust | [github.com/modelcontextprotocol/rust-sdk](https://github.com/modelcontextprotocol/rust-sdk) | Anthropic/Community | アクティブ | +| Dart | [https://github.com/leehack/mcp_dart](https://github.com/leehack/mcp_dart) | Flutter Community | アクティブ | + +これらのSDKは、MCPプロトコルとの作業を簡素化する言語固有の抽象化を提供し、低レベルのプロトコル詳細を扱うことなく、サーバーやクライアントのコアロジックの実装に集中できるようにします。 + +## 次のステップ + +MCPでできることの表面をかじっただけですが、すでに基本的なサーバーが動作しています。実際、ブラウザでMCPクライアントを使用して接続しています。 + +次のセクションでは、LLMからサーバーに接続する方法を見ていきます。 diff --git a/units/ja/unit1/unit1-recap.mdx b/units/ja/unit1/unit1-recap.mdx new file mode 100644 index 0000000..3d4f841 --- /dev/null +++ b/units/ja/unit1/unit1-recap.mdx @@ -0,0 +1,45 @@ +# Unit1 recap + +## Model Context Protocol (MCP) + +The MCP is a standardized protocol designed to connect AI models with external tools, data sources, and environments. It addresses the limitations of existing AI systems by enabling interoperability and access to real-time information. + +## Key Concepts + +### Client-Server Architecture +MCP follows a client-server model where clients manage communication between users and servers. This architecture promotes modularity, allowing for easy addition of new servers without requiring changes to existing hosts. + +### Components +#### Host +The user-facing AI application that serves as the interface for end-users. + +##### Client +A component within the host application responsible for managing communication with a specific MCP server. Clients maintain 1:1 connections with servers and handle protocol-level details. + +#### Server +An external program or service that provides access to tools, data sources, or services via the MCP protocol. Servers act as lightweight wrappers around existing functionalities. + +### Capabilities +#### Tools +Executable functions that can perform actions (e.g., sending messages, querying APIs). Tools are typically model-controlled and require user approval due to their ability to perform actions with side effects. + +#### Resources +Read-only data sources for context retrieval without significant computation. Resources are application-controlled and designed for data retrieval similar to GET endpoints in REST APIs. + +#### Prompts +Pre-defined templates or workflows that guide interactions between users, AI models, and available capabilities. Prompts are user-controlled and set the context for interactions. + +#### Sampling +Server-initiated requests for LLM processing, enabling server-driven agentic behaviors and potentially recursive or multi-step interactions. Sampling operations typically require user approval. + +### Communication Protocol +The MCP protocol uses JSON-RPC 2.0 as the message format for communication between clients and servers. Two primary transport mechanisms are supported: stdio (for local communication) and HTTP+SSE (for remote communication). Messages include requests, responses, and notifications. + +### Discovery Process +MCP allows clients to dynamically discover available tools, resources, and prompts through list methods (e.g., `tools/list`). This dynamic discovery mechanism enables clients to adapt to the specific capabilities each server offers without requiring hardcoded knowledge of server functionality. + +### MCP SDKs +Official SDKs are available in various programming languages for implementing MCP clients and servers. These SDKs handle protocol-level communication, capability registration, and error handling, simplifying the development process. + +### Gradio Integration +Gradio allows easy creation of web interfaces that expose capabilities to the MCP protocol, making it accessible for both humans and AI models. This integration provides a human-friendly interface alongside AI-accessible tools with minimal code. diff --git a/units/ja/unit2/clients.mdx b/units/ja/unit2/clients.mdx new file mode 100644 index 0000000..02391c2 --- /dev/null +++ b/units/ja/unit2/clients.mdx @@ -0,0 +1,136 @@ +# MCPクライアントの構築 + +このセクションでは、異なるプログラミング言語を使用してMCPサーバーと相互作用できるクライアントを作成します。HuggingFace.jsを使用したJavaScriptクライアントとsmolagentsを使用したPythonクライアントの両方を実装します。 + +## MCPクライアントの設定 + +MCPサーバーとクライアントの効果的なデプロイには適切な設定が必要です。MCP仕様はまだ進化しているため、設定方法は進化する可能性があります。現在の設定のベストプラクティスに焦点を当てます。 + +### MCP設定ファイル + +MCPホストは設定ファイルを使用してサーバー接続を管理します。これらのファイルは、利用可能なサーバーとそれらへの接続方法を定義します。 + +設定ファイルは非常にシンプルで、理解しやすく、主要なMCPホスト間で一貫しています。 + +#### `mcp.json`構造 + +MCPの標準設定ファイルは`mcp.json`という名前です。基本的な構造は以下の通りです： + +```json +{ + "servers": [ + { + "name": "MCP Server", + "transport": { + "type": "sse", + "url": "http://localhost:7860/gradio_api/mcp/sse" + } + } + ] +} +``` + +この例では、SSEトランスポートを使用し、ポート7860で実行されているローカルGradioサーバーに接続する単一のサーバーが設定されています。 + + + +Gradioアプリがリモートサーバーで実行されていると仮定しているため、SSEトランスポート経由でGradioアプリに接続しています。しかし、ローカルスクリプトに接続したい場合は、`sse`トランスポートの代わりに`stdio`トランスポートを使用することがより良い選択肢です。 + + + +#### HTTP+SSEトランスポートの設定 + +HTTP+SSEトランスポートを使用するリモートサーバーの場合、設定にはサーバーURLが含まれます： + +```json +{ + "servers": [ + { + "name": "Remote MCP Server", + "transport": { + "type": "sse", + "url": "https://example.com/gradio_api/mcp/sse" + } + } + ] +} +``` + +この設定により、UIクライアントはMCPプロトコルを使用してGradio MCPサーバーと通信でき、フロントエンドとMCPサービス間のシームレスな統合を可能にします。 + +## UI MCPクライアントの設定 + +Gradio MCPサーバーで作業する場合、MCPプロトコルを使用してサーバーに接続するようにUIクライアントを設定できます。設定方法は以下の通りです： + +### 基本設定 + +以下の設定で`config.json`という新しいファイルを作成してください： + +```json +{ + "mcpServers": { + "mcp": { + "url": "http://localhost:7860/gradio_api/mcp/sse" + } + } +} +``` + +この設定により、UIクライアントはMCPプロトコルを使用してGradio MCPサーバーと通信でき、フロントエンドとMCPサービス間のシームレスな統合を可能にします。 + +## Cursor IDE内でのMCPクライアントの設定 + +Cursorは組み込みMCPサポートを提供し、デプロイされたMCPサーバーを開発環境に直接接続できます。 + +### 設定 + +Cursor設定（`Ctrl + Shift + J` / `Cmd + Shift + J`）→ **MCP**タブ→ **Add new global MCP server**を開きます： + +**macOS:** +```json +{ + "mcpServers": { + "sentiment-analysis": { + "command": "npx", + "args": [ + "-y", + "mcp-remote", + "https://YOURUSENAME-mcp-sentiment.hf.space/gradio_api/mcp/sse", + "--transport", + "sse-only" + ] + } + } +} +``` + +**Windows:** +```json +{ + "mcpServers": { + "sentiment-analysis": { + "command": "cmd", + "args": [ + "/c", + "npx", + "-y", + "mcp-remote", + "https://YOURUSENAME-mcp-sentiment.hf.space/gradio_api/mcp/sse", + "--transport", + "sse-only" + ] + } + } +} +``` + +### mcp-remoteを使用する理由 + +Cursorを含むほとんどのMCPクライアントは、現在stdioトランスポート経由のローカルサーバーのみをサポートしており、OAuth認証を使用するリモートサーバーはまだサポートしていません。`mcp-remote`ツールは以下の機能を提供するブリッジソリューションとして機能します： + +- マシン上でローカルに実行 +- CursorからリモートMCPサーバーへのリクエストを転送 +- 馴染みのある設定ファイル形式を使用 + +設定完了後、コードコメント、ユーザーフィードバック、プルリクエストの説明の分析などのタスクに、感情分析ツールを使用するようにCursorに求めることができます。 + diff --git a/units/ja/unit2/continue-client.mdx b/units/ja/unit2/continue-client.mdx new file mode 100644 index 0000000..e368ebb --- /dev/null +++ b/units/ja/unit2/continue-client.mdx @@ -0,0 +1,193 @@ +# ローカルおよびオープンソースモデルでのMCPの使用 + +このセクションでは、Ollamaのようなローカルツールと連携するAIコーディングアシスタント構築ツールであるContinueを使用して、MCPをローカルおよびオープンソースモデルと接続します。 + +## Continueのセットアップ + +ContinueはVS Code マーケットプレイスからインストールできます。 + + + +*Continueは[JetBrains](https://plugins.jetbrains.com/plugin/22707-continue)の拡張機能もあります。* + + + +### VS Code拡張機能 + +1. [Visual Studio MarketplaceのContinue拡張機能ページ](https://marketplace.visualstudio.com/items?itemName=Continue.continue)で`Install`をクリック +2. これによりVS CodeでContinue拡張機能ページが開き、そこで再度`Install`をクリックする必要があります +3. Continueロゴが左サイドバーに表示されます。より良い体験のために、Continueを右サイドバーに移動してください + +![sidebar vs code demo](https://docs.continue.dev/assets/images/move-to-right-sidebar-b2d315296198e41046fc174d8178f30a.gif) + +Continueの設定が完了したら、ローカルモデルを取得するためのOllamaのセットアップに移ります。 + +### ローカルモデル + +Continueと互換性のあるローカルモデルを実行する方法は多数あります。人気のある3つのオプションは、Ollama、Llama.cpp、LM Studioです。Ollamaは、ユーザーが大規模言語モデル（LLM）をローカルで簡単に実行できるオープンソースツールです。Llama.cppは、OpenAI互換サーバーも含むLLM実行用の高性能C++ライブラリです。LM Studioは、ローカルモデルを実行するためのグラフィカルインターフェースを提供します。 + +Hugging Face Hubからローカルモデルにアクセスし、すべての主要なローカル推論アプリのコマンドとクイックリンクを取得できます。 + +![hugging face hub](https://cdn-uploads.huggingface.co/production/uploads/64445e5f1bc692d87b27e183/d6XMR5q9DwVpdEKFeLW9t.png) + + + + +Llama.cppは、LLMを提供するための軽量でOpenAI API互換のHTTPサーバーである`llama-server`を提供しています。[Llama.cppリポジトリ](https://github.com/ggml-org/llama.cpp)の指示に従ってソースからビルドするか、システムで利用可能な場合は事前にビルドされたバイナリを使用できます。詳細については、[Llama.cppドキュメント](https://github.com/ggerganov/llama.cpp)をご確認ください。 + +`llama-server`を入手したら、次のようなコマンドでHugging Faceからモデルを実行できます： + +```bash +llama-server -hf unsloth/Devstral-Small-2505-GGUF:Q4_K_M +``` + + + +LM Studioは、Mac、Windows、Linuxで利用可能なアプリケーションで、グラフィカルインターフェースでオープンソースモデルをローカルで簡単に実行できます。開始するには： + +1. [ここをクリックしてLM Studioでモデルを開く](lmstudio://open_from_hf?model=unsloth/Devstral-Small-2505-GGUF) +2. モデルがダウンロードされたら、「Local Server」タブに移動し、「Start Server」をクリック + + +Ollamaを使用するには、[インストール](https://ollama.com/download)して、実行したいモデルを`ollama run`コマンドでダウンロードできます。 + +例えば、[Devstral-Small](https://huggingface.co/unsloth/Devstral-Small-2505-GGUF?local-app=ollama)モデルをダウンロードして実行するには： + +```bash +ollama run unsloth/devstral-small-2505-gguf:Q4_K_M +``` + + + + + +Continueは様々なローカルモデルプロバイダーをサポートしています。Ollama、Llama.cpp、LM Studio以外にも他のプロバイダーを使用できます。サポートされているプロバイダーの完全なリストと詳細な設定オプションについては、[Continueドキュメント](https://docs.continue.dev/customize/model-providers)をご参照ください。 + + + +組み込み機能としてツール呼び出し機能を持つモデル、つまりCodestral QwenやLlama 3.1xを使用することが重要です。 + +1. ワークスペースのトップレベルに`.continue/models`というフォルダを作成 +2. モデルプロバイダーを設定するためのファイルをこのフォルダに追加。例：`local-models.yaml` +3. Ollama、Llama.cpp、LM Studioのどれを使用しているかに応じて、以下の設定を追加 + + + +この設定は`llama-server`で提供される`llama.cpp`モデル用です。`model`フィールドは提供するモデルと一致させる必要があります。 + +```yaml +name: Llama.cpp model +version: 0.0.1 +schema: v1 +models: + - provider: llama.cpp + model: unsloth/Devstral-Small-2505-GGUF + apiBase: http://localhost:8080 + defaultCompletionOptions: + contextLength: 8192 # モデルに基づいて調整 + name: Llama.cpp Devstral-Small + roles: + - chat + - edit +``` + + +この設定はLM Studio経由で提供されるモデル用です。モデル識別子はLM Studioにロードされているものと一致させる必要があります。 + +```yaml +name: LM Studio Model +version: 0.0.1 +schema: v1 +models: + - provider: lmstudio + model: unsloth/Devstral-Small-2505-GGUF + name: LM Studio Devstral-Small + apiBase: http://localhost:1234/v1 + roles: + - chat + - edit +``` + + +この設定はOllamaモデル用です。 + +```yaml +name: Ollama Devstral model +version: 0.0.1 +schema: v1 +models: + - provider: ollama + model: unsloth/devstral-small-2505-gguf:Q4_K_M + defaultCompletionOptions: + contextLength: 8192 + name: Ollama Devstral-Small + roles: + - chat + - edit +``` + + + +デフォルトでは、各モデルには最大コンテキスト長があり、この場合は`128000`トークンです。このセットアップには、複数のMCPリクエストを実行するためのより大きなコンテキストウィンドウの使用が含まれ、より多くのトークンを処理できる必要があります。 + +## 動作の仕組み + +### ツールハンドシェイク + +ツールは、モデルが外部世界とインターフェースするための強力な方法を提供します。これらは名前と引数スキーマを持つJSONオブジェクトとしてモデルに提供されます。例えば、`filepath`引数を持つ`read_file`ツールは、モデルに特定のファイルの内容を要求する能力を与えます。 + +![autonomous agents diagram](https://gist.github.com/user-attachments/assets/c7301fc0-fa5c-4dc4-9955-7ba8a6587b7a) + +以下のハンドシェイクは、エージェントがツールを使用する方法を説明しています： + +1. エージェントモードでは、使用可能なツールが`user`チャットリクエストと一緒に送信されます +2. モデルは応答にツール呼び出しを含めることを選択できます +3. ユーザーが許可を与えます。そのツールのポリシーが`Automatic`に設定されている場合、このステップはスキップされます +4. Continueは組み込み機能またはその特定のツールを提供するMCPサーバーを使用してツールを呼び出します +5. Continueは結果をモデルに送り返します +6. モデルが応答し、場合によっては別のツール呼び出しを行い、ステップ2が再び始まります + +Continueは複数のローカルモデルプロバイダーをサポートしています。異なるタスクに異なるモデルを使用したり、必要に応じてモデルを切り替えたりできます。このセクションではローカルファーストソリューションに焦点を当てていますが、ContinueはOpenAI、Anthropic、Microsoft/Azure、Mistralなどの人気プロバイダーでも動作します。独自のモデルプロバイダーを実行することも可能です。 + +### MCPとのローカルモデル統合 + +すべてのセットアップが完了したので、既存のMCPサーバーを追加しましょう。以下は、アシスタントで使用する新しいMCPサーバーをセットアップする簡単な例です： + +1. ワークスペースのトップレベルに`.continue/mcpServers`というフォルダを作成 +2. このフォルダに`playwright-mcp.yaml`というファイルを追加 +3. `playwright-mcp.yaml`に以下の内容を書き込んで保存 + +```yaml +name: Playwright mcpServer +version: 0.0.1 +schema: v1 +mcpServers: + - name: Browser search + command: npx + args: + - "@playwright/mcp@latest" +``` + +次のコマンドでMCPサーバーをテストしてください： + +``` +1. playwrightを使用して、https://news.ycombinator.com に移動してください。 + +2. ホームページの上位4つの投稿のタイトルとURLを抽出してください。 + +3. プロジェクトのルートディレクトリにhn.txtという名前のファイルを作成してください。 + +4. このリストをhn.txtファイルにプレーンテキストとして保存してください。各行にはタイトルとURLをハイフンで区切って含めてください。 + +コードや指示を出力せず、タスクを完了して完了時に確認してください。 +``` + +結果として、現在の作業ディレクトリに`hn.txt`という生成されたファイルが作成されます。 + +![mcp output example](https://deploy-preview-6060--continuedev.netlify.app/assets/images/mcp-playwright-50b192a2ff395f7a6cc11618c5e2d5b1.png) + +## まとめ + +ContinueをLlama 3.1のようなローカルモデルやMCPサーバーと組み合わせることで、最先端のAI機能を活用しながら、コードとデータをプライベートに保つ強力な開発ワークフローを手に入れました。 + +このセットアップにより、Web自動化からファイル管理まで、すべて完全にローカルマシンで実行される専門ツールでAIアシスタントをカスタマイズする柔軟性が得られます。開発ワークフローを次のレベルに引き上げる準備はできましたか？[Continue Hub MCP探索ページ](https://hub.continue.dev/explore/mcp)から異なるMCPサーバーを試してみることから始めて、ローカルAIがいかにコーディング体験を変革するかを発見してください。 \ No newline at end of file diff --git a/units/ja/unit2/gradio-client.mdx b/units/ja/unit2/gradio-client.mdx new file mode 100644 index 0000000..8108af2 --- /dev/null +++ b/units/ja/unit2/gradio-client.mdx @@ -0,0 +1,176 @@ +# MCPクライアントとしてのGradio + +前のセクションでは、Gradioを使用してMCPサーバーを作成し、MCPクライアントを使用してそれに接続する方法を探りました。このセクションでは、GradioをMCPクライアントとして使用してMCPサーバーに接続する方法を探ります。 + + + +GradioはUIクライアントとMCPサーバーの作成に最も適していますが、MCPクライアントとして使用してそれをUIとして公開することも可能です。 + + + +前のセクションで作成したものと類似したMCPサーバーに接続しますが、追加機能があり、それを使用して質問に答えます。 + +## GradioでのMCPクライアント + +### サンプルMCPサーバーへの接続 + +Hugging Faceで既に実行されているサンプルMCPサーバーに接続してみましょう。この例では[こちらのサーバー](https://huggingface.co/spaces/abidlabs/mcp-tool-http)を使用します。これはMCPツールのコレクションを含むスペースです。 + +```python +from smolagents.mcp_client import MCPClient + +with MCPClient( + {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"} +) as tools: + # リモートサーバーからのツールが利用可能 + print("\n".join(f"{t.name}: {t.description}" for t in tools)) + +``` + +
+出力 +
+
+prime_factors: Compute the prime factorization of a positive integer.
+generate_cheetah_image: Generate a cheetah image.
+image_orientation: Returns whether image is portrait or landscape.
+sepia: Apply a sepia filter to the input image.
+
+
+
+ +### GradioからMCPサーバーへの接続 + +素晴らしい、サンプルMCPサーバーに接続できました。次に、それをサンプルアプリケーションで使用する必要があります。 + +まず、まだインストールしていない場合は、`smolagents`、Gradio、mcp-clientライブラリをインストールする必要があります： + +```bash +pip install "smolagents[mcp]" "gradio[mcp]" mcp fastmcp +``` + +次に、必要なライブラリをインポートし、MCPクライアントを使用してMCPサーバーに接続するシンプルなGradioインターフェースを作成できます。 + +```python +import gradio as gr +import os + +from mcp import StdioServerParameters +from smolagents import InferenceClientModel, CodeAgent, ToolCollection, MCPClient +``` + +次に、MCPサーバーに接続し、質問に答えるために使用できるツールを取得します。 + +```python +mcp_client = MCPClient( + {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"} # これは前のセクションで作成したMCPクライアントです +) +tools = mcp_client.get_tools() +``` + +ツールを取得したので、それらを使用して質問に答えるシンプルなエージェントを作成できます。今のところ、シンプルな`InferenceClientModel`と`smolagents`のデフォルトモデルを使用します。 + +InferenceClientModelにapi_keyを渡すことが重要です。huggingfaceアカウントからトークンにアクセスできます。[こちらを確認してください。](https://huggingface.co/docs/hub/en/security-tokens)そして、環境変数`HF_TOKEN`でアクセストークンを設定してください。 + +```python +model = InferenceClientModel(token=os.getenv("HF_TOKEN")) +agent = CodeAgent(tools=[*tools], model=model) +``` + +次に、エージェントを使用して質問に答えるシンプルなGradioインターフェースを作成できます。 + +```python +demo = gr.ChatInterface( + fn=lambda message, history: str(agent.run(message)), + type="messages", + examples=["68の素因数分解"], + title="MCPツールを使用したエージェント", + description="これはMCPツールを使用して質問に答えるシンプルなエージェントです。" +) + +demo.launch() +``` + +それで完了です！MCPクライアントを使用してMCPサーバーに接続し、質問に答えるシンプルなGradioインターフェースを作成しました。 + + + +## 完全な例 + +以下は、GradioでのMCPクライアントの使用の完全な例です： + +```python +import gradio as gr +import os + +from smolagents import InferenceClientModel, CodeAgent, MCPClient + + +try: + mcp_client = MCPClient( + {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"} + ) + tools = mcp_client.get_tools() + + model = InferenceClientModel(token=os.getenv("HUGGINGFACE_API_TOKEN")) + agent = CodeAgent(tools=[*tools], model=model, additional_authorized_imports=["json", "ast", "urllib", "base64"]) + + demo = gr.ChatInterface( + fn=lambda message, history: str(agent.run(message)), + type="messages", + examples=["次のテキストの感情を分析してください 'This is awesome'"], + title="MCPツールを使用したエージェント", + description="これはMCPツールを使用して質問に答えるシンプルなエージェントです。", + ) + + demo.launch() +finally: + mcp_client.disconnect() +``` + +`finally`ブロックでMCPクライアントを閉じていることに注意してください。これは、MCPクライアントがプログラム終了時に閉じる必要がある長寿命オブジェクトであるため重要です。 + +## Hugging Face Spacesへのデプロイ + +前のセクションで行ったように、サーバーを他の人が利用できるようにするために、Hugging Face Spacesにデプロイできます。 +Gradio MCPクライアントをHugging Face Spacesにデプロイするには： + +1. Hugging Faceで新しいスペースを作成： + - huggingface.co/spacesに移動 + - 「Create new Space」をクリック + - SDKとして「Gradio」を選択 + - スペースに名前を付ける（例：「mcp-client」） + +2. コード内のMCPサーバーURLを更新： + +```python +mcp_client = MCPClient( + {"url": "https://abidlabs-mcp-tool-http.hf.space/gradio_api/mcp/sse"} +) +``` + +3. `requirements.txt`ファイルを作成： +```txt +gradio[mcp] +smolagents[mcp] +``` + +4. コードをスペースにプッシュ： +```bash +git init +git add app.py requirements.txt +git commit -m "Initial commit" +git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/mcp-client +git push -u origin main +``` + +注意：リモートオリジンを追加する際は、AccessTokenで追加するために[password-git-deprecation](https://huggingface.co/blog/password-git-deprecation)を参照してください。 + +## まとめ + +このセクションでは、GradioをMCPクライアントとして使用してMCPサーバーに接続する方法を探りました。また、MCPクライアントをHugging Face Spacesにデプロイする方法も見ました。 \ No newline at end of file diff --git a/units/ja/unit2/gradio-server.mdx b/units/ja/unit2/gradio-server.mdx new file mode 100644 index 0000000..6819eb0 --- /dev/null +++ b/units/ja/unit2/gradio-server.mdx @@ -0,0 +1,193 @@ +# Gradio MCPサーバーの構築 + +このセクションでは、Gradioを使用して感情分析MCPサーバーを作成します。このサーバーは、Webインターフェースを通じたユーザーとMCPプロトコルを通じたAIモデルの両方が使用できる感情分析ツールを公開します。 + +## Gradio MCP統合の紹介 + +Gradioは、Python関数をMCPツールに自動変換することでMCPサーバーを作成する簡単な方法を提供します。`launch()`で`mcp_server=True`を設定すると、Gradioは以下を実行します： + +1. 関数をMCPツールに自動変換 +2. 入力コンポーネントをツール引数スキーマにマッピング +3. 出力コンポーネントからレスポンス形式を決定 +4. クライアント・サーバー通信用にHTTP+SSE上のJSON-RPCを設定 +5. WebインターフェースとMCPサーバーエンドポイントの両方を作成 + +## プロジェクトのセットアップ + +まず、プロジェクト用の新しいディレクトリを作成し、必要な依存関係をセットアップしましょう： + +```bash +mkdir mcp-sentiment +cd mcp-sentiment +python -m venv venv +source venv/bin/activate # Windowsの場合: venv\Scripts\activate +pip install "gradio[mcp]" textblob +``` + +## サーバーの作成 + +> Hugging face spacesはスペースを構築するためにapp.pyファイルが必要です。そのため、Pythonファイルの名前はapp.pyでなければなりません。 + +以下のコードで`app.py`という新しいファイルを作成してください： + +```python +import json +import gradio as gr +from textblob import TextBlob + +def sentiment_analysis(text: str) -> str: + """ + 与えられたテキストの感情を分析します。 + + Args: + text (str): 分析するテキスト + + Returns: + str: 極性、主観性、評価を含むJSON文字列 + """ + blob = TextBlob(text) + sentiment = blob.sentiment + + result = { + "polarity": round(sentiment.polarity, 2), # -1 (ネガティブ) から 1 (ポジティブ) + "subjectivity": round(sentiment.subjectivity, 2), # 0 (客観的) から 1 (主観的) + "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral" + } + + return json.dumps(result) + +# Gradioインターフェースを作成 +demo = gr.Interface( + fn=sentiment_analysis, + inputs=gr.Textbox(placeholder="分析するテキストを入力..."), + outputs=gr.Textbox(), # gr.JSON()からgr.Textbox()に変更 + title="テキスト感情分析", + description="TextBlobを使用してテキストの感情を分析します" +) + +# インターフェースとMCPサーバーを起動 +if __name__ == "__main__": + demo.launch(mcp_server=True) +``` + +## コードの理解 + +主要なコンポーネントを分解してみましょう： + +1. **関数定義**: + - `sentiment_analysis`関数はテキスト入力を受け取り、辞書を返します + - TextBlobを使用して感情を分析します + - docstringはGradioがMCPツールスキーマを生成するために重要です + - 型ヒント（`str`と`dict`）は入出力スキーマを定義するのに役立ちます + +2. **Gradioインターフェース**: + - `gr.Interface`はWeb UIとMCPサーバーの両方を作成します + - 関数は自動的にMCPツールとして公開されます + - 入力と出力コンポーネントがツールのスキーマを定義します + - JSON出力コンポーネントは適切なシリアライゼーションを保証します + +3. **MCPサーバー**: + - `mcp_server=True`を設定するとMCPサーバーが有効になります + - サーバーは`http://localhost:7860/gradio_api/mcp/sse`で利用可能になります + - 環境変数を使用して有効にすることもできます： + ```bash + export GRADIO_MCP_SERVER=True + ``` + +## サーバーの実行 + +以下を実行してサーバーを起動します： + +```bash +python app.py +``` + +WebインターフェースとMCPサーバーの両方が実行されていることを示す出力が表示されるはずです。Webインターフェースは`http://localhost:7860`で、MCPサーバーは`http://localhost:7860/gradio_api/mcp/sse`で利用可能です。 + +## サーバーのテスト + +サーバーは2つの方法でテストできます： + +1. **Webインターフェース**: + - ブラウザで`http://localhost:7860`を開きます + - テキストを入力し、"送信"をクリックします + - 感情分析結果が表示されるはずです + +2. **MCPスキーマ**: + - `http://localhost:7860/gradio_api/mcp/schema`を訪問します + - これはクライアントが使用するMCPツールスキーマを表示します + - Gradioアプリのフッターにある"View API"リンクでもこれを見つけることができます + +## トラブルシューティングティップス + +1. **型ヒントとDocstring**: + - 関数パラメーターと戻り値に必ず型ヒントを提供してください + - 各パラメーターに対して"Args:"ブロックを含むdocstringを含めてください + - これはGradioが正確なMCPツールスキーマを生成するのに役立ちます + +2. **文字列入力**: + - 迷ったときは、入力引数を`str`として受け入れてください + - 関数内で目的の型に変換してください + - これはMCPクライアントとの互換性を向上させます + +3. **SSEサポート**: + - 一部のMCPクライアントはSSEベースのMCPサーバーをサポートしていません + - その場合、`mcp-remote`を使用してください： + ```json + { + "mcpServers": { + "gradio": { + "command": "npx", + "args": [ + "mcp-remote", + "http://localhost:7860/gradio_api/mcp/sse" + ] + } + } + } + ``` + +4. **接続問題**: + - 接続問題が発生した場合、クライアントとサーバーの両方を再起動してみてください + - サーバーが実行され、アクセス可能であることを確認してください + - MCPスキーマが期待されるURLで利用可能であることを確認してください + +## Hugging Face Spacesへのデプロイ + +サーバーを他の人が利用できるようにするために、Hugging Face Spacesにデプロイできます： + +1. Hugging Faceで新しいSpaceを作成します： + - huggingface.co/spacesにアクセスします + - "Create new Space"をクリックします + - SDKとして"Gradio"を選択します + - スペースに名前を付けます（例："mcp-sentiment"） + +2. `requirements.txt`ファイルを作成します： +```txt +gradio[mcp] +textblob +``` + +3. コードをSpaceにプッシュします： +```bash +git init +git add app.py requirements.txt +git commit -m "Initial commit" +git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/mcp-sentiment +git push -u origin main +``` + +MCPサーバーは以下で利用可能になります： +``` +https://YOUR_USERNAME-mcp-sentiment.hf.space/gradio_api/mcp/sse +``` + +## 次のステップ + +MCPサーバーが実行されたので、それとインタラクトするクライアントを作成します。次のセクションでは、以下を行います： + +1. Tiny AgentsにインスパイアされたHuggingFace.jsベースのクライアントを作成 +2. SmolAgentsベースのPythonクライアントを実装 +3. デプロイされたサーバーで両方のクライアントをテスト + +最初のクライアントの構築に移りましょう！ diff --git a/units/ja/unit2/introduction.mdx b/units/ja/unit2/introduction.mdx new file mode 100644 index 0000000..de6a74e --- /dev/null +++ b/units/ja/unit2/introduction.mdx @@ -0,0 +1,64 @@ +# エンドツーエンドMCPアプリケーションの構築 + +MCPコースのユニット2へようこそ！ + +このユニットでは、完全なMCPアプリケーションをゼロから構築し、Gradioを使用したサーバーの作成と複数のクライアントとの接続に焦点を当てます。このハンズオンアプローチにより、MCPエコシステム全体の実践的な経験を積むことができます。 + + + +このユニットでは、GradioとHuggingFace hubを使用してシンプルなMCPサーバーとクライアントを構築します。次のユニットでは、実際のユースケースに取り組むより複雑なサーバーを構築します。 + + + +## 学習内容 + +このユニットでは、以下のことを学びます： + +- Gradioの組み込みMCPサポートを使用したMCPサーバーの作成 +- AIモデルで使用できる感情分析ツールの構築 +- 異なるクライアント実装を使用したサーバーへの接続： + - HuggingFace.jsベースのクライアント + - PythonのSmolAgentsベースのクライアント +- MCPサーバーのHugging Face Spacesへのデプロイ +- 完全なシステムのテストとデバッグ + +このユニットの終わりには、プロトコルの力と柔軟性を実証する動作するMCPアプリケーションを手にすることになります。 + +## 前提条件 + +このユニットを進める前に、以下を確認してください： + +- ユニット1を完了しているか、MCPの概念の基本的な理解がある +- PythonとJavaScript/TypeScriptの両方に慣れている +- APIとクライアント・サーバーアーキテクチャの基本的な理解がある +- 開発環境を持っている： + - Python 3.10+ + - Node.js 18+ + - Hugging Faceアカウント（デプロイ用） + +## エンドツーエンドプロジェクト + +サーバー、クライアント、デプロイメントの3つの主要部分からなる感情分析アプリケーションを構築します。 + +![sentiment analysis application](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit2/1.png) + +### サーバー側 + +- Gradioを使用して`gr.Interface`経由でWebインターフェースとMCPサーバーを作成 +- TextBlobを使用した感情分析ツールの実装 +- HTTPとMCPプロトコルの両方を通じたツールの公開 + +### クライアント側 + +- HuggingFace.jsクライアントの実装 +- または、smolagents Pythonクライアントの作成 +- 異なるクライアント実装で同じサーバーを使用する方法の実証 + +### デプロイメント + +- サーバーのHugging Face Spacesへのデプロイ +- デプロイされたサーバーと連携するためのクライアントの設定 + +## 始めましょう！ + +初めてのエンドツーエンドMCPアプリケーションを構築する準備はできましたか？開発環境の設定とGradio MCPサーバーの作成から始めましょう。 \ No newline at end of file diff --git a/units/ja/unit2/tiny-agents.mdx b/units/ja/unit2/tiny-agents.mdx new file mode 100644 index 0000000..f175d19 --- /dev/null +++ b/units/ja/unit2/tiny-agents.mdx @@ -0,0 +1,271 @@ +# MCPとHugging Face HubでTiny Agentsを構築 + +これまでGradioでMCPサーバーを構築し、MCPクライアントの作成について学んできました。今度は、感情分析ツールとシームレスに相互作用できるエージェントを構築して、エンドツーエンドアプリケーションを完成させましょう。このセクションは、[Tiny Agents](https://huggingface.co/blog/tiny-agents)プロジェクトをベースにしており、Gradio感情分析サーバーのようなサービスに接続できるMCPクライアントをデプロイする超シンプルな方法を実証しています。 + +このユニット2の最終演習では、前のセクションで構築したGradioベースの感情分析サーバーを含む、あらゆるMCPサーバーと通信できるTypeScript（JS）とPythonの両方のMCPクライアントを実装する方法をご案内します。これにより、感情分析ツールを公開するGradio MCPサーバーの構築から、他の機能と組み合わせてこのツールを使用できる柔軟なエージェントの作成まで、エンドツーエンドMCPアプリケーションフローが完成します。 + +![meme](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/tiny-agents/thumbnail.jpg) +
画像クレジット https://x.com/adamdotdev
+ +## インストール + +Tiny Agentsを構築するために必要なパッケージをインストールしましょう。 + + + +Claude Desktopなど、一部のMCPクライアントはまだSSEベースのMCPサーバーをサポートしていません。その場合、[mcp-remote](https://github.com/geelen/mcp-remote)のようなツールを使用できます。まずNode.jsをインストールしてください。次に、独自のMCPクライアント設定に以下を追加してください： + + + +Tiny Agentはコマンドライン環境でMCPサーバーを実行できます。これを行うには、`npm`をインストールし、`npx`でサーバーを実行する必要があります。**PythonとJavaScriptの両方でこれらが必要です。** + +`npm`で`npx`をインストールしましょう。`npm`がインストールされていない場合は、[npmドキュメント](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm)を確認してください。 + +```bash +# npxをインストール +npm install -g npx +``` + +次に、`mcp-remote`パッケージをインストールする必要があります。 + +```bash +npm i mcp-remote +``` + + + + +JavaScriptの場合、`tiny-agents`パッケージをインストールする必要があります。 + +```bash +npm install @huggingface/tiny-agents +``` + + + + +Pythonの場合、必要なコンポーネントをすべて取得するために、`mcp`エクストラ付きの最新バージョンの`huggingface_hub`をインストールする必要があります。 + +```bash +pip install "huggingface_hub[mcp]>=0.32.0" +``` + + + + +## コマンドラインでのTiny Agents MCPクライアント + +基本的なTiny Agentを作成するために、[ユニット1](../unit1/mcp-clients.mdx)の例を繰り返しましょう。Tiny AgentsはJSON設定ファイルに基づいてコマンドラインからMCPクライアントを作成できます。 + + + + +基本的なTiny Agentでプロジェクトをセットアップしましょう。 + +```bash +mkdir my-agent +touch my-agent/agent.json +``` + +JSONファイルは次のようになります： + +```json +{ + "model": "Qwen/Qwen2.5-72B-Instruct", + "provider": "nebius", + "servers": [ + { + "type": "stdio", + "config": { + "command": "npx", + "args": [ + "mcp-remote", + "http://localhost:7860/gradio_api/mcp/sse" // これは前のセクションで作成したMCPサーバーです + ] + } + } + ] +} +``` + +次のコマンドでエージェントを実行できます： + +```bash +npx @huggingface/tiny-agents run ./my-agent +``` + + + + +基本的なTiny Agentでプロジェクトをセットアップしましょう。 + +```bash +mkdir my-agent +touch my-agent/agent.json +cd my-agent +``` + +JSONファイルは次のようになります： + +```json +{ + "model": "Qwen/Qwen2.5-72B-Instruct", + "provider": "nebius", + "servers": [ + { + "type": "stdio", + "config": { + "command": "npx", + "args": [ + "mcp-remote", + "http://localhost:7860/gradio_api/mcp/sse" + ] + } + } + ] +} +``` + +次のコマンドでエージェントを実行できます： + +```bash +tiny-agents run agent.json +``` + + + + +ここで、Gradio MCPサーバーに接続できる基本的なTiny Agentができました。これには、モデル、プロバイダー、サーバー設定が含まれています。 + +| フィールド | 説明 | +|-------|-------------| +| `model` | エージェントに使用するオープンソースモデル | +| `provider` | エージェントに使用する推論プロバイダー | +| `servers` | エージェントに使用するサーバー。Gradio MCPサーバー用に`mcp-remote`サーバーを使用します。 | + + + +Tiny Agentsでローカルで実行されているオープンソースモデルを使用することもできます。ローカル推論サーバーを起動する場合は + +```json +{ + "model": "Qwen/Qwen3-32B", + "endpointUrl": "http://localhost:1234/v1", + "servers": [ + { + "type": "stdio", + "config": { + "command": "npx", + "args": [ + "mcp-remote", + "http://localhost:1234/v1/mcp/sse" + ] + } + } + ] +} +``` + +ここで、ローカルモデルに接続できるTiny Agentができました。これには、モデル、エンドポイントURL（`http://localhost:1234/v1`）、サーバー設定が含まれています。エンドポイントはOpenAI互換のエンドポイントである必要があります。 + + + +## カスタムTiny Agents MCPクライアント + +Tiny AgentsとGradio MCPサーバーの両方を理解したので、それらがどのように連携するかを見てみましょう！MCPの美しさは、前のセクションのGradioベースの感情分析サーバーを含む、あらゆるMCP互換サーバーとエージェントが相互作用するための標準化された方法を提供することです。 + +### Tiny AgentsでGradioサーバーを使用 + +このユニットで前に構築したGradio感情分析サーバーにTiny Agentを接続するには、サーバーのリストに追加するだけです。エージェント設定を変更する方法は次のとおりです： + + + + +```ts +const agent = new Agent({ + provider: process.env.PROVIDER ?? "nebius", + model: process.env.MODEL_ID ?? "Qwen/Qwen2.5-72B-Instruct", + apiKey: process.env.HF_TOKEN, + servers: [ + // ... 既存のサーバー ... + { + command: "npx", + args: [ + "mcp-remote", + "http://localhost:7860/gradio_api/mcp/sse" // あなたのGradio MCPサーバー + ] + } + ], +}); +``` + + + + +```python +import os + +from huggingface_hub import Agent + +agent = Agent( + model="Qwen/Qwen2.5-72B-Instruct", + provider="nebius", + servers=[ + { + "command": "npx", + "args": [ + "mcp-remote", + "http://localhost:7860/gradio_api/mcp/sse" # あなたのGradio MCPサーバー + ] + } + ], +) +``` + + + + +これで、エージェントは他のツールと組み合わせて感情分析ツールを使用できます！例えば、以下のことができます： +1. ファイルシステムサーバーを使用してファイルからテキストを読み取る +2. Gradioサーバーを使用してその感情を分析する +3. 結果をファイルに書き戻す + +### デプロイメントの考慮事項 + +Gradio MCPサーバーをHugging Face Spacesにデプロイする場合、デプロイされたスペースを指すようにエージェント設定のサーバーURLを更新する必要があります： + +```json +{ + command: "npx", + args: [ + "mcp-remote", + "https://YOUR_USERNAME-mcp-sentiment.hf.space/gradio_api/mcp/sse" + ] +} +``` + +これにより、エージェントはローカルだけでなく、どこからでも感情分析ツールを使用できます！ + +## まとめ：完全なエンドツーエンドMCPアプリケーション + +このユニットでは、MCPの基本理解から完全なエンドツーエンドアプリケーションの構築まで進んできました： + +1. 感情分析ツールを公開するGradio MCPサーバーを作成しました +2. MCPクライアントを使用してこのサーバーに接続する方法を学びました +3. ツールと相互作用できるTypeScriptとPythonの小さなエージェントを構築しました + +これは、Model Context Protocolの力を実証しています - 慣れ親しんだフレームワーク（Gradioなど）を使用して専門ツールを作成し、標準化されたインターフェース（MCP）を通じてそれらを公開し、エージェントがこれらのツールを他の機能と組み合わせてシームレスに使用できます。 + +構築した完全なフローにより、エージェントは以下のことができます： +- 複数のツールプロバイダーに接続 +- 利用可能なツールを動的に発見 +- カスタム感情分析ツールを使用 +- ファイルシステムアクセスやWebブラウジングなどの他の機能と組み合わせる + +このモジュラーアプローチが、柔軟なAIアプリケーションを構築するためのMCPの強力さの源です。 + +## 次のステップ + +- [Python](https://huggingface.co/blog/python-tiny-agents)と[TypeScript](https://huggingface.co/blog/tiny-agents)のTiny Agentsブログ投稿をチェック +- [Tiny Agentsドキュメント](https://huggingface.co/docs/huggingface.js/main/en/tiny-agents/README)を確認 +- Tiny Agentsで何かを構築しましょう！ \ No newline at end of file diff --git a/units/ja/unit3/build-mcp-server-solution-walkthrough.mdx b/units/ja/unit3/build-mcp-server-solution-walkthrough.mdx new file mode 100644 index 0000000..00ec5e5 --- /dev/null +++ b/units/ja/unit3/build-mcp-server-solution-walkthrough.mdx @@ -0,0 +1,444 @@ +# Unit 3 解決方法ウォークスルー: MCPでプルリクエストエージェントを構築する + +## 概要 + +このウォークスルーでは、Unit 3のプルリクエストエージェントの完全な解決方法を説明します。これは、コードの変更を分析し、CI/CDパイプラインを監視し、チームコミュニケーションを自動化することで、開発者がより良いプルリクエストを作成できるようにするMCPサーバーです。この解決方法では、MCPの3つのプリミティブ（Tools、Resources、Prompts）が実際のワークフローで連携して動作することを実演します。 + +## アーキテクチャ概要 + +PRエージェントは、完全な自動化システムを段階的に構築する相互接続されたモジュールで構成されています： + +1. **Build MCP Server** - PRテンプレート提案用のToolsを含む基本サーバー +2. **Smart File Analysis** - プロジェクトコンテキスト用のResourcesを使用した拡張分析 +3. **GitHub Actions Integration** - 標準化されたPromptsを使用したCI/CD監視 +4. **Hugging Face Hub Integration** - モデルデプロイメントとデータセットPRワークフロー +5. **Slack Notification** - すべてのMCPプリミティブを統合したチームコミュニケーション + +## モジュール1: Build MCP Server + +### 構築するもの +MCPツールを使用してファイルの変更を分析し、適切なPRテンプレートを提案する最小限のMCPサーバー。 + +### 主要コンポーネント + +#### 1. サーバー初期化 (`server.py`) +```python +# サーバーは3つの重要なツールを登録します： +# - analyze_file_changes: 変更されたファイルに関する構造化データを返す +# - get_pr_templates: メタデータ付きの利用可能なテンプレートをリストする +# - suggest_template: インテリジェントなテンプレート推奨を提供する +``` + +サーバーはMCP SDKを使用してこれらのツールをClaude Codeに公開し、情報を収集して使用するPRテンプレートについて賢明な決定を下すことができます。 + +#### 2. ファイル分析ツール +`analyze_file_changes`ツールはgit diffを検査して以下を特定します： +- ファイルタイプと拡張子 +- 変更されたファイル数 +- 追加/削除された行数 +- 一般的なパターン（テスト、設定、ドキュメント） + +この構造化データにより、Claudeは決定ロジックをハードコーディングすることなく変更の性質を理解できます。 + +#### 3. テンプレート管理 +テンプレートは`templates/`ディレクトリにmarkdownファイルとして保存されます： +- `bug.md` - バグ修正用 +- `feature.md` - 新機能用 +- `docs.md` - ドキュメント更新用 +- `refactor.md` - コードリファクタリング用 + +各テンプレートには、Claudeが分析に基づいて入力できるプレースホルダーが含まれています。 + +### Claudeがこれらのツールを使用する方法 + +1. Claudeは`analyze_file_changes`を呼び出して何が変更されたかを理解します +2. `get_pr_templates`を使用して利用可能なオプションを確認します +3. 分析データを使用して`suggest_template`を呼び出します +4. 理由付きの推奨を受け取ります +5. 特定の変更に基づいてテンプレートをカスタマイズできます + +### 学習成果 +- ツール登録とスキーマの理解 +- 構造化データを使用してClaudeに決定を委ねる +- データ収集と決定ロジックの分離 + +## モジュール2: Smart File Analysis + +### 構築するもの +MCPリソースを使用してプロジェクトコンテキストとチームガイドラインを提供する拡張ファイル分析。 + +### 主要コンポーネント + +#### 1. リソース登録 +サーバーは4つのタイプのリソースを公開します： +```python +# リソースは以下への読み取り専用アクセスを提供します： +# - file://templates/ - PRテンプレートファイル +# - file://project-context/ - コーディング標準、規約 +# - git://recent-changes/ - コミット履歴とパターン +# - team://guidelines/ - レビュープロセスと標準 +``` + +#### 2. プロジェクトコンテキストリソース +`project-context/`ディレクトリには以下が含まれます： +- `coding-standards.md` - 言語固有の規約 +- `review-guidelines.md` - レビュアーが確認する項目 +- `architecture.md` - システム設計パターン +- `dependencies.md` - サードパーティライブラリポリシー + +Claudeはこれらを読み取ってプロジェクト固有の要件を理解できます。 + +#### 3. Git履歴分析 +`git://recent-changes/`リソースは以下を提供します： +- 最近のコミットメッセージとパターン +- 一般的なPRタイトルと説明 +- チームメンバーの貢献パターン +- 履歴的なテンプレート使用状況 + +これにより、Claudeはチームの慣行と一致するテンプレートを提案できます。 + +### Claudeがリソースを使用する方法 + +1. `team://guidelines/review-process.md`を読み取ってPR要件を理解します +2. `file://project-context/coding-standards.md`にアクセスしてスタイルガイドを取得します +3. `git://recent-changes/`を分析してチームパターンと一致させます +4. このコンテキストをファイル分析と組み合わせて、より良い提案を行います + +### 拡張された意思決定 +リソースにより、Claudeは以下のことができるようになります： +- チーム規約と一致するテンプレートを提案する +- PRにプロジェクト固有の要件を含める +- 説明でコーディング標準を参照する +- 履歴的なチーム慣行と整合させる + +### 学習成果 +- リソースURIの設計とスキーマ +- プロジェクト知識をAIにアクセス可能にする +- コンテキストを意識した意思決定 +- 自動化とチーム標準のバランス + +## モジュール3: GitHub Actions Integration + +### 構築するもの +webhookと一貫したチームコミュニケーションのための標準化されたプロンプトを使用したリアルタイムCI/CD監視。 + +### 主要コンポーネント + +#### 1. Webhookサーバー +Cloudflare TunnelでGitHub Actionsイベントを受信します： +```python +# Webhookエンドポイントは以下を処理します： +# - workflow_runイベント +# - check_runイベント +# - pull_requestステータス更新 +# - デプロイメント通知 +``` + +#### 2. プロンプトテンプレート +4つの標準化されたプロンプトが一貫性を保証します： +- **"Analyze CI Results"** - テスト失敗とビルドエラーを処理 +- **"Generate Status Summary"** - 人間が読みやすいステータス更新を作成 +- **"Create Follow-up Tasks"** - 結果に基づいて次のステップを提案 +- **"Draft Team Notification"** - 異なる対象者向けの更新をフォーマット + +#### 3. イベント処理パイプライン +1. GitHubからwebhookを受信 +2. イベントデータを解析し、関連情報を抽出 +3. イベントタイプに基づいて適切なプロンプトを使用 +4. 標準化されたレスポンスを生成 +5. チーム通知のために保存 + +### Claudeがプロンプトを使用する方法 + +プロンプト使用例： +```python +# テストが失敗した場合、Claudeは"Analyze CI Results"プロンプトを使用します： +prompt_data = { + "event_type": "workflow_run", + "status": "failure", + "failed_jobs": ["unit-tests", "lint"], + "error_logs": "...", + "pr_context": {...} +} + +# Claudeが生成するもの： +# - 根本原因分析 +# - 提案修正 +# - 影響評価 +# - 次のステップ +``` + +### 標準化されたワークフロー +プロンプトは、誰が作業していても以下を保証します： +- CI失敗が一貫して分析される +- ステータス更新がチーム形式に従う +- フォローアップアクションがプロセスと整合する +- 通知が必要な情報を含む + +### 学習成果 +- Webhook統合パターン +- 一貫性のためのプロンプトエンジニアリング +- イベント駆動アーキテクチャ +- チームワークフローの標準化 + +## モジュール4: Hugging Face Hub Integration + +### 構築するもの +LLMとデータセットPR用のHugging Face Hub統合、言語モデルを扱うチーム向けの専用ワークフローを追加。 + +### 主要コンポーネント + +#### 1. Hub固有のツール +```python +# Hugging Faceワークフロー用のツール： +# - analyze_model_changes: LLMファイル変更を検出 +# - validate_dataset_format: 訓練データコンプライアンスをチェック +# - generate_model_card: モデルドキュメンテーションを作成/更新 +# - suggest_hub_template: LLM/データセット用PRテンプレート +``` + +#### 2. Hubリソース +```python +# Hubコンテキスト用のリソース： +# - hub://model-cards/ - LLMカードテンプレートと例 +# - hub://dataset-formats/ - 訓練データ仕様 +# - hub://community-standards/ - Hubコミュニティガイドライン +# - hub://license-info/ - ライセンス互換性チェック +``` + +#### 3. LLM固有のプロンプト +```python +# LLMワークフロー用のプロンプト： +# - "Analyze Model Changes" - LLM更新を理解 +# - "Generate Benchmark Summary" - 評価メトリクスを作成 +# - "Check Dataset Quality" - 訓練データを検証 +# - "Draft Model Card Update" - ドキュメンテーションを更新 +``` + +### Hub固有のワークフロー + +PRがLLMファイルを変更する場合： +1. **ツール**: `analyze_model_changes`がモデルアーキテクチャの変更を検出 +2. **リソース**: `hub://model-cards/llm-template.md`を読み取り +3. **プロンプト**: "Generate Benchmark Summary"が評価セクションを作成 +4. **ツール**: `generate_model_card`がドキュメンテーションを更新 +5. **リソース**: `hub://license-info/`で互換性をチェック + +### データセットPR処理 +訓練データ更新の場合： +- 形式の一貫性を検証 +- データ品質メトリクスをチェック +- データセットカードを更新 +- 適切なレビュアーを提案 + +### 学習成果 +- Hugging Face Hub API統合 +- LLM固有のPRワークフロー +- モデルとデータセットドキュメンテーション +- コミュニティ標準準拠 + +## モジュール5: Slack Notification + +### 構築するもの +完全なワークフロー自動化のためにTools、Resources、Promptsを組み合わせた自動チーム通知。 + +### 主要コンポーネント + +#### 1. コミュニケーションツール +```python +# チーム更新用の3つのツール： +# - send_slack_message: チームチャンネルに投稿 +# - get_team_members: 通知対象者を特定 +# - track_notification_status: 配信を監視 +``` + +#### 2. チームリソース +```python +# チームデータ用のリソース： +# - team://members/ - 開発者プロフィールと設定 +# - slack://channels/ - チャンネル設定 +# - notification://templates/ - メッセージ形式 +``` + +#### 3. 通知プロンプト +```python +# コミュニケーション用のプロンプト： +# - "Format Team Update" - メッセージを適切にスタイル +# - "Choose Communication Channel" - 適切な対象者を選択 +# - "Escalate if Critical" - 緊急問題を処理 +``` + +### 統合例 + +重要なPRでCIが失敗した場合： +1. **ツール**: `get_team_members`がPR作成者とレビュアーを特定 +2. **リソース**: `team://members/{user}/preferences`が通知設定をチェック +3. **プロンプト**: "Format Team Update"が適切なメッセージを作成 +4. **ツール**: `send_slack_message`が適切なチャンネルに配信 +5. **リソース**: `notification://templates/ci-failure`が一貫した形式を保証 +6. **プロンプト**: "Escalate if Critical"が追加アラートが必要かを判断 + +### インテリジェントルーティング +システムは以下を考慮します： +- チームメンバーの空き状況（カレンダーリソースから） +- 通知設定（メール vs Slack） +- メッセージの緊急度（PRラベルに基づく） +- タイムゾーンと勤務時間 + +### 学習成果 +- プリミティブ統合パターン +- 複雑なワークフローオーケストレーション +- 自動化と人間のニーズのバランス +- 本番環境対応のエラー処理 + +## 完全なワークフロー例 + +典型的なPRでのすべてのコンポーネントの連携方法： + +1. **開発者がPRを作成** + - GitHub webhookがサーバーをトリガー + - ツール: `analyze_file_changes`がdiffを調査 + - リソース: チームガイドラインとプロジェクトコンテキストを読み取り + - プロンプト: 最適なPRテンプレートを提案 + +2. **CI/CDパイプライン実行** + - Webhookがワークフローイベントを受信 + - プロンプト: "Analyze CI Results"が結果を処理 + - リソース: チームエスカレーションポリシーをチェック + - ツール: GitHubでPRステータスを更新 + +3. **Hugging Face Hub統合** + - ツール: LLM/データセット変更を検出 + - リソース: Hubガイドラインを読み取り + - プロンプト: モデルカード更新を生成 + - ツール: Hub標準に対して検証 + +4. **チーム通知** + - ツール: 関連チームメンバーを特定 + - リソース: 通知設定を読み取り + - プロンプト: 適切なメッセージをフォーマット + - ツール: Slackチャンネル経由で送信 + +5. **フォローアップアクション** + - プロンプト: "Create Follow-up Tasks"が次のステップを生成 + - ツール: 必要に応じてGitHub issueを作成 + - リソース: ドキュメンテーションにリンク + - すべてのプリミティブがシームレスに連携 + +## テスト戦略 + +### 単体テスト +各モジュールには包括的な単体テストが含まれます： +- ツールスキーマ検証 +- リソースURI解析 +- プロンプトテンプレートレンダリング +- 統合シナリオ + +### 統合テスト +エンドツーエンドテストのカバー範囲： +- 完全なPRワークフロー +- エラー回復シナリオ +- 負荷下でのパフォーマンス +- セキュリティ検証 + +### テスト構造 +``` +tests/ +├── unit/ +│ ├── test_tools.py +│ ├── test_resources.py +│ ├── test_prompts.py +│ └── test_integration.py +├── integration/ +│ ├── test_workflow.py +│ ├── test_webhooks.py +│ └── test_notifications.py +└── fixtures/ + ├── sample_events.json + └── mock_responses.json +``` + +## 解決方法の実行 + +### ローカル開発環境設定 +1. **MCPサーバーを開始**: `python server.py` +2. **Claude Codeを設定**: サーバーをMCP設定に追加 +3. **Cloudflare Tunnelを設定**: `cloudflared tunnel --url http://localhost:3000` +4. **Webhookを設定**: GitHubリポジトリにトンネルURLを追加 +5. **ワークフローをテスト**: PRを作成して自動化を確認 + +### 設定 +簡単なセットアップのためのファイルベース設定： +- `.env`ファイルのGitHubトークン +- 設定のSlack webhook +- `templates/`のテンプレートカスタマイゼーション +- すべての設定を1箇所に + +## 一般的なパターンとベストプラクティス + +### ツール設計 +- ツールを集中的で単一目的に保つ +- AI解釈のための構造化データを返す +- 包括的なエラーメッセージを含める +- ツールスキーマにバージョンを付ける + +### リソース整理 +- 明確なURI階層を使用 +- リソース発見を実装 +- 頻繁にアクセスされるリソースをキャッシュ +- すべてのリソースをバージョン管理 + +### プロンプトエンジニアリング +- プロンプトを具体的だが柔軟にする +- コンテキストと例を含める +- 様々な入力でテスト +- プロンプトライブラリを維持 + +### 統合パターン +- 疎結合のためのイベントを使用 +- サーキットブレーカーを実装 +- バックオフ付きリトライを追加 +- すべての外部呼び出しを監視 + +## トラブルシューティングガイド + +### 一般的な問題 + +1. **Webhookがイベントを受信しない** + - Cloudflare Tunnelが実行中かチェック + - GitHub webhook設定を確認 + - シークレットが一致することを確認 + +2. **ツールがClaudeに表示されない** + - ツールスキーマを検証 + - サーバー登録をチェック + - MCP接続を確認 + +3. **リソースにアクセスできない** + - ファイル権限を確認 + - URI形式をチェック + - リソース登録を確認 + +4. **プロンプトが一貫しない結果を生成** + - プロンプトテンプレートを確認 + - 提供されたコンテキストをチェック + - 入力形式を検証 + +## 次のステップと拡張 + +### 潜在的な拡張 +1. より多くのコード分析ツールを追加（複雑さ、セキュリティ） +2. より多くのコミュニケーションプラットフォームと統合 +3. カスタムワークフロー定義を追加 +4. PR自動マージ機能を実装 + +### 学習パス +- **次**: Unit 4 - このサーバーをリモートデプロイ +- **上級**: カスタムMCPプロトコル拡張 +- **エキスパート**: マルチサーバーオーケストレーション + +## 結論 + +このPRエージェントは、MCPの3つのプリミティブが連携して動作する力を実演しています。ツールは機能を提供し、リソースはコンテキストを提供し、プロンプトは一貫性を保証します。組み合わせることで、チーム標準を維持しながら開発者の生産性を向上させるインテリジェントな自動化システムを作成します。 + +モジュラーアーキテクチャにより、各コンポーネントを独立して理解、テスト、拡張できる一方で、統合は本番環境のMCPサーバーで使用する実際のパターンを示しています。 \ No newline at end of file diff --git a/units/ja/unit3/build-mcp-server.mdx b/units/ja/unit3/build-mcp-server.mdx new file mode 100644 index 0000000..48e52f9 --- /dev/null +++ b/units/ja/unit3/build-mcp-server.mdx @@ -0,0 +1,379 @@ +# モジュール1: MCPサーバーの構築 + +## CodeCraft StudiosでのPRカオス + +CodeCraft Studiosでの最初の週で、あなたはあらゆる開発者が身震いするような光景を目の当たりにしています。チームのプルリクエストは以下のような感じです： + +- "stuff" +- "more changes" +- "fix" +- "update things" + +一方で、レビュワーが何が変更されたのか、なぜ変更されたのかを理解できないため、コードレビューのバックログが溜まり続けています。バックエンドチームのSarahは「various improvements」が実際に何を意味するのかを理解するのに30分を費やし、フロントエンドのMikeは「small fix」を理解するために47個のファイルを調べなければなりませんでした。 + +チームは、より良いPR説明が必要だと分かっていますが、みんな機能の出荷に忙しくて詳細な説明を書く時間がありません。作業速度を落とすことなく支援するソリューションが必要です。 + +**あなたのミッション**: コードの変更を分析し、自動的に有用な説明を提案するインテリジェントなPRエージェントを構築することです。 + +### スクリーンキャスト: PRの問題の実際の様子 😬 + + + +**見えるもの**: CodeCraft Studioでの実際のPRで、タイトルは「various improvements」、説明は単に「Fixed some stuff and made updates」と書かれています。典型的ですね？ + +**混乱**: チームメートが苦労している様子を見てください： +- **Sarah**（3時間前）：「何が修正されたの？Userモデルの変更は見えるけど、これがバグの対処なのか機能追加なのか分からない」 +- **Jamie**（3時間前）：「4つのサービスにまたがって8つのファイル...これらの変更は関連している？レビュー中に何に焦点を当てるべき？」 + +**痛いポイント**: スクリーンキャストは実際のdiffを示しています—複数のサービスに散らばった8つのファイルで、コンテキストがゼロです。レビュワーが自分でストーリーをつなぎ合わせなければならず、貴重な時間を無駄にし、重要な問題を見逃す可能性があります。 + +**なぜこれが重要か**: これは、あなたのMCPサーバーが解決する正確なPRカオスです！このモジュールの終わりまでに、これらの不可解なPRを、みんなの生活を楽にする明確で実行可能な説明に変えることができるでしょう。 + +## 構築するもの + +この最初のモジュールでは、CodeCraft Studiosの自動化システムの基盤を作成します：チームがプルリクエストを書く方法を変革するMCPサーバーです。このモジュールは、モジュール2と3で構築するコアMCPコンセプトに焦点を当てています。 + +### スクリーンキャスト: あなたのPRエージェントが問題を解決！ 🚀 + + + +**動作中のソリューション**: あなたのMCPサーバーがどのようにPRカオスを明確さに変えるかを見てください： +1. **`analyze_file_changes`** - すべての変更を取得（8つのファイルにわたって453行！） +2. **`get_per_templates`** - Claudeに選択する7つのテンプレートを表示 +3. **`suggest_template`** - Claudeが「Feature」を選択（賢い選択！） + +**見えるもの**: Claudeは単にテンプレートを選ぶだけでなく： +- 実際に何が変更されたかの明確な要約を書く +- セキュリティの問題を発見（うわ、ハッシュ化されていないパスワード！） +- フォローアップ作業のナイスなTo-Doリストを作成 +- 最初に修正する必要があるものの優先順位付けも行う + +**「ワオ」の瞬間** ✨: ほんの数秒で、あなたのMCPサーバーがClaude同じブランチを実際に何が起こっているかを説明するPRに変えるのを助けます。もう混乱したレビュワーはいません、もう「これは何をするの？」というコメントもありません。 + +**これがあなたが構築するもの**: PRの恐怖をPRの喜びに変えるツール—始めましょう！ + +## 学習すること + +この基礎モジュールで、あなたは以下をマスターします： +- **FastMCPを使用した基本的なMCPサーバーの作成方法** - モジュール2と3の構築ブロック +- **データ取得と分析のためのMCPツールの実装** - ユニット3全体で使用するコアプリミティブ +- **生データに基づいてClaudeに知的な決定をさせる** - すべてのMCP開発の重要な原則 +- **MCPサーバーのテストと検証** - 信頼できるツールを構築するための必須スキル + +## 概要 + +あなたのPRエージェントは、MCP開発の重要な原則を使用してCodeCraft Studiosの問題を解決します：良いPRを作る硬直したルールをハードコーディングする代わりに、Claudeに生のgitデータを提供し、適切な説明を知的に提案させます。 + +このアプローチが機能する理由： +- **柔軟な分析**: Claudeは単純なルールが見逃すコンテキストを理解できる +- **自然言語**: 提案が人間らしく、ロボット的でない +- **適応性**: どのコードベースやコーディングスタイルでも機能する + +自動化システム全体のパターンを確立する3つの必須ツールを実装します： + +1. **analyze_file_changes** - gitの差分情報と変更されたファイルを取得（データ収集） +2. **get_pr_templates** - 利用可能なPRテンプレートをリスト（リソース管理） +3. **suggest_template** - Claudeが最も適切なテンプレートを推奨できるようにする（知的意思決定） + +## 始める + +### 前提条件 + +- Python 3.10以上 +- Gitがインストールされており、テスト用のgitリポジトリがある +- uvパッケージマネージャー（[インストールガイド](https://docs.astral.sh/uv/getting-started/installation/)） + +### スターターコード + +スターターコードリポジトリをクローンします： + +```bash +git clone https://github.com/huggingface/mcp-course.git +``` + +スターターコードディレクトリに移動します： + +```bash +cd mcp-course/projects/unit3/build-mcp-server/starter +``` + +依存関係をインストールします： + + + +このプロジェクト用に仮想環境を作成することをお勧めします： + +```bash +uv venv .venv +source .venv/bin/activate # Windowsの場合： .venv\Scripts\activate +``` + + +```bash +uv sync --all-extras +``` + +### あなたのタスク + +これはあなたの最初のハンズオンMCP開発体験です！`server.py`を開き、TODOコメントに従って3つのツールを実装してください。スターターコードは基本構造を提供しているので、あなたがする必要があるのは： + +1. **`analyze_file_changes`を実装** - gitコマンドを実行してdiffデータを返す + - ⚠️ **重要**: トークン制限エラーに遭遇する可能性があります（レスポンスあたり最大25,000トークン） + - これは適切な出力管理を教える実世界の制約です + - 解決策については下の「大きな出力の処理」セクションを参照してください + - ⚠️ **注意**: GitコマンドはデフォルトでMCPサーバーのディレクトリで実行されます。詳細については下の「作業ディレクトリの考慮事項」を参照してください +2. **`get_pr_templates`を実装** - PRテンプレートを管理して返す +3. **`suggest_template`を実装** - 変更タイプをテンプレートにマッピングする + +完璧にすることを心配する必要はありません - ユニットを進むにつれてこれらのスキルを磨いていきます。 + +### 設計哲学 + +ファイル拡張子や硬直したパターンに基づいて変更を分類する従来のシステムとは異なり、あなたの実装は以下を行うべきです： + +- Claudeに生のgitデータ（差分、ファイルリスト、統計）を提供する +- Claudeに実際のコード変更を分析させる +- Claudeが知的なテンプレート提案を行えるようにする +- ロジックをシンプルに保つ - Claudeが複雑さを処理する + + + +**MCPの哲学**: ツールに複雑なロジックを構築する代わりに、Claudeに豊富なデータを提供し、その知性に決定を任せます。これにより、従来のルールベースシステムよりもコードがシンプルで柔軟になります。 + + + +## 実装のテスト + +### 1. コードの検証 + +検証スクリプトを実行して実装をチェックします： + +```bash +uv run python validate_starter.py +``` + +### 2. ユニットテストの実行 + +提供されたテストスイートで実装をテストします： + +```bash +uv run pytest test_server.py -v +``` + +### 3. Claude Codeでのテスト + +Claude Codeでサーバーを直接設定します： + +```bash +# MCPサーバーをClaude Codeに追加 +claude mcp add pr-agent -- uv --directory /absolute/path/to/starter run server.py + +# サーバーが設定されていることを確認 +claude mcp list +``` + +その後： +1. gitリポジトリで何かを変更する +2. Claudeに質問：「私の変更を分析してPRテンプレートを提案できますか？」 +3. Claudeがあなたのツールを使って知的な提案を提供するのを見る + + + +**よくある最初のエラー**: 「MCPツールレスポンスが最大許可トークン数（25000）を超えています」というエラーが出た場合、これは予想されることです！大きなリポジトリは大量のdiffを生成する可能性があります。これは貴重な学習の瞬間です - 解決策については「大きな出力の処理」セクションを参照してください。 + + + +## 一般的なパターン + +### ツール実装パターン + +```python +@mcp.tool() +async def tool_name(param1: str, param2: bool = True) -> str: + """Claudeのためのツール説明。 + + Args: + param1: パラメータの説明 + param2: デフォルト値を持つオプションパラメータ + """ + # あなたの実装 + result = {"key": "value"} + return json.dumps(result) +``` + +### エラーハンドリング + +常に潜在的なエラーを適切に処理します： + +```python +try: + result = subprocess.run(["git", "diff"], capture_output=True, text=True) + return json.dumps({"output": result.stdout}) +except Exception as e: + return json.dumps({"error": str(e)}) +``` + + + +**エラーハンドリング**: エラーの場合でも、ツールからは常に有効なJSONを返してください。Claudeは何が間違ったかを理解し、ユーザーに有用な回答を提供するために構造化データが必要です。 + + + +### 大きな出力の処理（重要な学習の瞬間！） + + + +**実世界の制約**: MCPツールには、レスポンスあたり25,000トークンの制限があります。大きなgit diffは簡単にこの制限を10倍以上超える可能性があります！これはプロダクションMCP開発にとって重要な教訓です。 + + + +`analyze_file_changes`を実装する際、このエラーに遭遇する可能性があります： +``` +Error: MCP tool response (262521 tokens) exceeds maximum allowed tokens (25000) +``` + +**なぜこれが起こるか：** +- 単一ファイルの変更が数千行になることがある +- エンタープライズリポジトリには大規模なリファクタリングがある +- Git diffはデフォルトで完全なコンテキストを含む +- JSONエンコーディングがオーバーヘッドを追加する + +これは重要な原則を教えてくれます：**常に出力制限を念頭に置いてツールを設計する**。解決策は以下の通りです： + +```python +@mcp.tool() +async def analyze_file_changes(base_branch: str = "main", + include_diff: bool = True, + max_diff_lines: int = 500) -> str: + """スマートな出力制限付きでファイル変更を分析。 + + Args: + base_branch: 比較対象のブランチ + include_diff: 実際のdiffを含めるかどうか + max_diff_lines: 含める最大diff行数（デフォルト500） + """ + try: + # diffを取得 + result = subprocess.run( + ["git", "diff", f"{base_branch}...HEAD"], + capture_output=True, + text=True + ) + + diff_output = result.stdout + diff_lines = diff_output.split('\n') + + # 必要に応じてスマート切り捨て + if len(diff_lines) > max_diff_lines: + truncated_diff = '\n'.join(diff_lines[:max_diff_lines]) + truncated_diff += f"\n\n... 出力が切り捨てられました。{len(diff_lines)}行のうち{max_diff_lines}行を表示 ..." + diff_output = truncated_diff + + # サマリー統計を取得 + stats_result = subprocess.run( + ["git", "diff", "--stat", f"{base_branch}...HEAD"], + capture_output=True, + text=True + ) + + return json.dumps({ + "stats": stats_result.stdout, + "total_lines": len(diff_lines), + "diff": diff_output if include_diff else "include_diff=trueを使用してdiffを表示", + "files_changed": self._get_changed_files(base_branch) + }) + + except Exception as e: + return json.dumps({"error": str(e)}) +``` + +**大きな出力のベストプラクティス：** +1. **ページネーションの実装**: 大きな結果をページに分割 +2. **フィルタリングオプションの追加**: ユーザーが特定のファイルやディレクトリを要求できるようにする +3. **まずサマリーを提供**: 完全なコンテンツの前に統計を返す +4. **段階的開示を使用**: 高レベル情報から始めて、詳細に掘り下げを許可 +5. **合理的なデフォルトを設定**: ほとんどのケースで機能する合理的な制限をデフォルトにする + +## 作業ディレクトリの考慮事項 + +デフォルトでは、MCPサーバーは、Claudeの現在の作業ディレクトリではなく、インストールディレクトリでコマンドを実行します。これは、gitコマンドが間違ったリポジトリを分析する可能性があることを意味します！ + +これを解決するために、MCPは[roots](https://modelcontextprotocol.io/docs/concepts/roots)を提供します - クライアントが関連ディレクトリについてサーバーに通知する方法です。Claude Codeは自動的に作業ディレクトリをrootとして提供します。 + +ツールでこれにアクセスする方法は以下の通りです： + +```python +@mcp.tool() +async def analyze_file_changes(...): + # rootsからClaudeの作業ディレクトリを取得 + context = mcp.get_context() + roots_result = await context.session.list_roots() + + # FileUrlオブジェクトからパスを抽出 + working_dir = roots_result.roots[0].uri.path + + # すべてのgitコマンドに使用 + result = subprocess.run( + ["git", "diff", "--name-status"], + capture_output=True, + text=True, + cwd=working_dir # Claudeのディレクトリで実行！ + ) +``` + +これにより、ツールがMCPサーバーのインストール場所ではなく、Claudeが実際に作業しているリポジトリで動作することが保証されます。 + +## トラブルシューティング + +- **インポートエラー**: `uv sync`を実行したことを確認してください +- **Gitエラー**: gitリポジトリ内にいることを確認してください +- **出力なし**: MCPサーバーはstdio経由で通信します - Claude Desktopでテストしてください +- **JSONエラー**: すべてのツールは有効なJSON文字列を返す必要があります +- **トークン制限超過**: これは大きなdiffでは予想されることです！上記のような出力制限を実装してください +- **「レスポンスが大きすぎる」エラー**: `max_diff_lines`パラメータを追加するか、`include_diff=false`を設定してください +- **Gitコマンドが間違ったディレクトリで実行される**: MCPサーバーはデフォルトでClaudeの作業ディレクトリではなく、インストールディレクトリで実行されます。これを修正するには、[MCP roots](https://modelcontextprotocol.io/docs/concepts/roots)を使用してClaudeの現在のディレクトリにアクセスしてください： + ```python + # rootsからClaudeの作業ディレクトリを取得 + context = mcp.get_context() + roots_result = await context.session.list_roots() + working_dir = roots_result.roots[0].uri.path # FileUrlオブジェクトには.pathプロパティがある + + # subprocessコールで使用 + subprocess.run(["git", "diff"], cwd=working_dir) + ``` + Claude Codeは自動的に作業ディレクトリをrootとして提供し、MCPサーバーが正しい場所で動作できるようにします。 + +## 次のステップ + +おめでとうございます！ユニット3で続くすべての基盤となる、ツール付きの最初のMCPサーバーを構築しました。 + +### モジュール1で達成したこと： +- **MCPツールを作成** - Claudeに構造化データを提供 +- **コアMCP哲学を実装** - 生データからClaudeに知的決定をさせる +- **実用的なPRエージェントを構築** - コード変更を分析してテンプレートを提案できる +- **実世界の制約について学習** - 25,000トークン制限とその対処法 +- **テストパターンを確立** - 検証スクリプトとユニットテストを使用 + +### 再利用可能な重要パターン： +- **データ収集ツール** - 外部ソースから情報を収集 +- **知的分析** - Claudeが生データを処理して決定を下す +- **出力管理** - 有用性を保ちながら大きなレスポンスを切り捨てる +- **エラーハンドリング** - 構造化JSONレスポンスを返す +- **テスト戦略** - MCPサーバー開発のため + +### 次にすること： +1. **ソリューションを確認** - `/projects/unit3/build-mcp-server/solution/`で異なる実装アプローチを見る +2. **実装を比較** - 提供されたソリューションと比較 - 問題を解決する「正しい」方法は一つではありません +3. **ツールを徹底的にテスト** - 異なるタイプのコード変更で試して、Claudeがどう適応するかを見る +4. **モジュール2に進む** - リアルタイムwebhook機能を追加し、ワークフロー標準化のためのMCPプロンプトについて学ぶ + +モジュール2は、ここで作成したサーバーを直接基盤として構築し、静的ファイル分析ツールを補完する動的イベントハンドリングを追加します！ + +### ストーリーは続く... +あなたのPRエージェントが機能することで、CodeCraft Studiosの開発者はすでにより良いプルリクエストを書いています。しかし来週、あなたは新しい挑戦に直面します：重要なCI/CD障害が気づかれずに通り過ぎています。モジュール2では、これらの問題がプロダクションに到達する前にキャッチするリアルタイム監視を追加します。 + +## 追加リソース + +- [MCP ドキュメント](https://modelcontextprotocol.io/) +- [FastMCP ガイド](https://modelcontextprotocol.io/quickstart/server) +- ソリューションウォークスルー： `unit3/build-mcp-server-solution-walkthrough.md` \ No newline at end of file diff --git a/units/ja/unit3/certificate.mdx b/units/ja/unit3/certificate.mdx new file mode 100644 index 0000000..b21c62b --- /dev/null +++ b/units/ja/unit3/certificate.mdx @@ -0,0 +1,19 @@ +# Get your certificate! + +Well done! You've completed Unit 3 of the MCP course. Now it's time to take the exam to get your certificate. + +Below is a quiz to check your understanding of the unit. + + + + + +If you're struggling to use the quiz above, go to the space directly [on the Hugging Face Hub](https://huggingface.co/spaces/mcp-course/unit_3_quiz). If you find errors, you can report them in the space's [Community tab](https://huggingface.co/spaces/mcp-course/unit_3_quiz/discussions). + + + diff --git a/units/ja/unit3/conclusion.mdx b/units/ja/unit3/conclusion.mdx new file mode 100644 index 0000000..16654a4 --- /dev/null +++ b/units/ja/unit3/conclusion.mdx @@ -0,0 +1,147 @@ +# Unit 3 Conclusion: The CodeCraft Studios Transformation + +## Mission Accomplished! + +Congratulations! You've successfully transformed CodeCraft Studios from a chaotic startup into a well-oiled development machine. Let's see how far you've come: + +### Before Your Automation System: +- ❌ PRs with descriptions like "stuff" and "fix" +- ❌ Critical bugs reaching production undetected +- ❌ Teams working in silos, duplicating effort +- ❌ Weekend debugging sessions for already-fixed issues + +### After Your Automation System: +- ✅ Clear, helpful PR descriptions that save reviewers time +- ✅ Real-time CI/CD monitoring that catches failures immediately +- ✅ Smart team notifications that keep everyone informed +- ✅ Developers focused on building features, not fighting process problems + +The CodeCraft Studios team now has a complete automation system that demonstrates what's possible when you combine MCP's flexibility with Claude's intelligence. + +## How You Solved Each Challenge + +Your three-module journey tackled real problems that every development team faces: + +### Module 1: Solved the PR Chaos +*"Help developers write better pull requests without slowing them down"* +- **PR Agent** with intelligent file analysis +- **Core MCP concepts**: Tools, data collection, and Claude integration +- **Design philosophy**: Provide raw data, let Claude make intelligent decisions +- **Result**: Clear PR descriptions that help reviewers understand changes + +### Module 2: Caught the Silent Failures +*"Never let another critical bug slip through unnoticed"* +- **Webhook server** for capturing GitHub Actions events +- **MCP Prompts** for standardized workflow guidance +- **Event storage system** using simple JSON files +- **Result**: Real-time CI/CD monitoring that prevents production issues + +### Module 3: Bridged the Communication Gap +*"Keep the whole team informed about what's happening"* +- **Slack integration** for team notifications +- **Message formatting** using Claude's intelligence +- **Tools + Prompts combination** for powerful automation +- **Result**: Smart notifications that eliminate information silos + +## Key MCP Concepts You've Learned + +### MCP Primitives +- **Tools**: For data access and external API calls +- **Prompts**: For consistent workflow guidance and formatting +- **Integration patterns**: How Tools and Prompts work together + +### Architecture Patterns +- **Separation of concerns**: MCP server vs webhook server +- **File-based event storage**: Simple, reliable, testable +- **Claude as the intelligence layer**: Making decisions from raw data + +### Development Best Practices +- **Error handling**: Returning structured JSON even for failures +- **Security**: Environment variables for sensitive credentials +- **Testing**: Validation scripts and manual testing workflows + +## Real-World Applications + +The patterns you've learned can be applied to many automation scenarios: + + + +**Beyond CI/CD**: The Tools + Prompts pattern works for customer support automation, content moderation, data analysis workflows, and any scenario where you need intelligent processing of external data. + + + +### Common Patterns from Unit 3 +1. **Data Collection** → Tools that gather information +2. **Intelligent Analysis** → Claude processes the data +3. **Formatted Output** → Prompts guide consistent presentation +4. **External Integration** → Tools interact with APIs and services + +## Next Steps + +### Immediate Actions +1. **Experiment** with your workflow automation - try different GitHub events +2. **Extend** the system with additional integrations (Discord, email, etc.) +3. **Share** your MCP server with teammates for real project use + +### Advanced Exploration +- **Scale up**: Handle multiple repositories or teams +- **Add persistence**: Use databases for larger event volumes +- **Create dashboards**: Build web interfaces for your automation +- **Explore other MCP clients**: Beyond Claude Code and Claude Desktop + +### Community Involvement +- **Contribute** to the MCP ecosystem with your own servers +- **Share patterns** you discover with the community +- **Build on** existing MCP servers and extend their capabilities + +## Key Takeaways + + + +**MCP Philosophy**: The most effective MCP servers don't try to be smart - they provide Claude with rich, structured data and let Claude's intelligence do the heavy lifting. This makes your code simpler and more flexible. + + + +### Technical Insights +- **Simple is powerful**: JSON file storage can handle many use cases +- **Claude as orchestrator**: Let Claude coordinate between your tools +- **Prompts for consistency**: Use prompts to ensure reliable output formats + +### Development Insights +- **Start small**: Build one tool at a time, test thoroughly +- **Think in workflows**: Design tools that work well together +- **Plan for humans**: Your automation should help teams, not replace them + +## Resources for Continued Learning + +### MCP Documentation +- [Official MCP Protocol](https://modelcontextprotocol.io/) +- [Python SDK Reference](https://github.com/modelcontextprotocol/python-sdk) +- [FastMCP Framework](https://gofastmcp.com/) + +### Community Resources +- [MCP Server Directory](https://modelcontextprotocol.io/servers) +- [Example Implementations](https://github.com/modelcontextprotocol) +- [Community Discord](https://discord.gg/modelcontextprotocol) + +--- + +## The CodeCraft Studios Success Story + +Three weeks ago, CodeCraft Studios was struggling with: +- Unclear pull requests causing review delays +- Critical bugs slipping into production +- Teams working in isolation and duplicating effort + +Today, they have an intelligent automation system that: +- **Helps developers** write clear, helpful PR descriptions automatically +- **Monitors CI/CD pipelines** and alerts the team to issues immediately +- **Keeps everyone informed** with smart, contextual team notifications + +You've built more than just an MCP server - you've created a solution that transforms how development teams work together. + +## Your MCP Journey Continues + +The patterns you learned at CodeCraft Studios can solve countless other automation challenges. Whether you're building customer service tools, data analysis pipelines, or any system that needs intelligent processing, you now have the foundation to create powerful, adaptive solutions with MCP. + +The future of intelligent automation is in your hands. What will you build next? 🚀 \ No newline at end of file diff --git a/units/ja/unit3/github-actions-integration.mdx b/units/ja/unit3/github-actions-integration.mdx new file mode 100644 index 0000000..5747c6e --- /dev/null +++ b/units/ja/unit3/github-actions-integration.mdx @@ -0,0 +1,268 @@ +# Module 2: GitHub Actions Integration + +## The Silent Failures Strike + +Week 2 at CodeCraft Studios. Your PR Agent from Module 1 is already helping developers write better pull requests - Sarah's latest PR had a clear description that saved Mike 20 minutes of investigation time. The team is thrilled! + +But then disaster strikes. + +A critical bug reaches production on Friday afternoon. The payment system is down, customers are complaining, and the team scrambles to investigate. After two stressful hours, they discover the root cause: a test failure in Tuesday's CI run that nobody noticed. + +"How did we miss this?" asks the team lead, scrolling through GitHub Actions. "The tests clearly failed, but with 47 repositories and dozens of daily commits, who has time to check every build?" + +The team realizes they need real-time visibility into their CI/CD pipeline, but manually checking GitHub Actions across all their projects isn't scalable. They need automation that watches for problems and alerts them immediately. + +**Your mission**: Extend your MCP server with webhook capabilities to monitor GitHub Actions and never let another failure slip through unnoticed. + +## What You'll Build + +This module bridges the gap between static file analysis (Module 1) and dynamic team notifications (Module 3). You'll add real-time capabilities that transform your PR Agent into a comprehensive development monitoring system. + +Building on the foundation you created in Module 1, you'll add: +- **Webhook server** to receive GitHub Actions events +- **New tools** for monitoring CI/CD status +- **MCP Prompts** that provide consistent workflow patterns +- **Real-time integration** with your GitHub repository + +### Screencast: Real-Time CI/CD Monitoring in Action! 🎯 + + + +**The Setup**: Watch how CodeCraft Studios' new system catches failures before they reach production: +1. **GitHub Webhooks** - See the actual webhook configuration that sends events to your server +2. **Failed Tests** - Those red X's that used to go unnoticed? Not anymore! +3. **Local Development** - The webhook server and Cloudflare tunnel working together + +**MCP Magic in Real-Time**: Claude responds to three key requests: +- **"What GitHub Actions events have we received?"** - Claude uses your new tools to check recent activity +- **"Analyze CI Results"** - Watch Claude dig into test failures and provide actionable insights +- **"Create Deployment Summary"** - See how MCP Prompts guide Claude to create team-friendly updates + +**The Silent Failures No More** 🚨: Remember that critical bug from Tuesday's failed test? With this system, Claude would have caught it immediately. The screencast shows exactly how your MCP server turns GitHub's raw webhook data into clear, actionable alerts. + +**What Makes This Special**: Your Module 1 PR Agent was static—it analyzed code when asked. This Module 2 enhancement is dynamic—it watches your CI/CD pipeline 24/7 and helps Claude provide real-time insights. No more Friday afternoon surprises! + +## Learning Objectives + +By the end of this module, you'll understand: +1. How to run a webhook server alongside an MCP server +2. How to receive and process GitHub webhooks +3. How to create MCP Prompts for standardized workflows +4. How to use Cloudflare Tunnel for local webhook testing + +## Prerequisites + +You'll build directly on your work from Module 1, so make sure you have: +- **Completed Module 1: Build MCP Server** - You'll be extending that same codebase +- **Basic understanding of GitHub Actions** - You should know what CI/CD workflows are +- **A GitHub repository with Actions enabled** - Even a simple workflow file works fine +- **Cloudflare Tunnel (cloudflared) installed** - This will expose your local webhook server to GitHub + +## Key Concepts + +### MCP Prompts + +Prompts are reusable templates that guide Claude through complex workflows. Unlike Tools (which Claude calls automatically), Prompts are user-initiated and provide structured guidance. + +Example use cases: +- Analyzing CI/CD results consistently +- Creating standardized deployment summaries +- Troubleshooting failures systematically + +### Webhook Integration + +Your MCP server will run two services: +1. The MCP server (communicates with Claude) +2. A webhook server on port 8080 (receives GitHub events) + +This allows Claude to react to real-time CI/CD events! + + + +**Architecture Insight**: Running separate services for MCP communication and webhook handling is a clean separation of concerns. The webhook server handles HTTP complexity while your MCP server focuses on data analysis and Claude integration. + + + +## Project Structure + +``` +github-actions-integration/ +├── starter/ # Your starting point +│ ├── server.py # Module 1 code + TODOs +│ ├── pyproject.toml +│ └── README.md +└── solution/ # Complete implementation + ├── server.py # Full webhook + prompts + ├── pyproject.toml + └── README.md +``` + +## Implementation Steps + +### Step 1: Set Up and Run Webhook Server + +Unlike Module 1 where you worked with existing files, this module introduces real-time event handling. The starter code includes: +- **Your Module 1 implementation** - All your existing PR analysis tools +- **A complete webhook server** (`webhook_server.py`) - Ready to receive GitHub events + +1. Install dependencies (same as Module 1): + ```bash + uv sync + ``` + +2. Start the webhook server (in a separate terminal): + ```bash + python webhook_server.py + ``` + +This server will receive GitHub webhooks and store them in `github_events.json`. + +**How webhook event storage works:** +- Each incoming GitHub webhook (push, pull request, workflow completion, etc.) is appended to the JSON file +- Events are stored with timestamps, making it easy to find recent activity +- The file acts as a simple event log that your MCP tools can read and analyze +- No database required - everything is stored in a simple, readable JSON format + +### Step 2: Connect to Event Storage + +Now you'll connect your MCP server (from Module 1) to the webhook data. This is much simpler than handling HTTP requests directly - the webhook server does all the heavy lifting and stores events in a JSON file. + +Add the path to read webhook events: + +```python +# File where webhook server stores events +EVENTS_FILE = Path(__file__).parent / "github_events.json" +``` + +The webhook server handles all the HTTP details - you just need to read the JSON file! This separation of concerns keeps your MCP server focused on what it does best. + + + +**Development Tip**: Working with files instead of HTTP requests makes testing much easier. You can manually add events to `github_events.json` to test your tools without setting up webhooks. + + + +### Step 3: Add GitHub Actions Tools + +Just like in Module 1 where you created tools for file analysis, you'll now create tools for CI/CD analysis. These tools will work alongside your existing PR analysis tools, giving Claude a complete view of both code changes and build status. + + + +**Note**: The starter code already includes the output limiting fix from Module 1, so you won't encounter token limit errors. Focus on the new concepts in this module! + + + +Implement two new tools: + +1. **`get_recent_actions_events`**: + - Read from `EVENTS_FILE` + - Return the most recent events (up to limit) + - Return empty list if file doesn't exist + +2. **`get_workflow_status`**: + - Read all events from file + - Filter for workflow_run events + - Group by workflow name and show latest status + +These tools let Claude analyze your CI/CD pipeline. + +### Step 4: Create MCP Prompts + +Now you'll add your first MCP Prompts! Unlike Tools (which Claude calls automatically), Prompts are templates that help users interact with Claude consistently. Think of them as "conversation starters" that guide Claude through complex workflows. + +While Module 1 focused on Tools for data access, this module introduces Prompts for workflow guidance. + +Implement four prompts that demonstrate different workflow patterns: + +1. **`analyze_ci_results`**: Comprehensive CI/CD analysis +2. **`create_deployment_summary`**: Team-friendly updates +3. **`generate_pr_status_report`**: Combined code + CI report +4. **`troubleshoot_workflow_failure`**: Systematic debugging + +Each prompt should return a string with clear instructions for Claude to follow. + +### Step 5: Test with Cloudflare Tunnel + +Now for the exciting part - testing your expanded MCP server with real GitHub events! You'll run multiple services together, just like in a real development environment. + +1. Start your MCP server (same command as Module 1): + ```bash + uv run server.py + ``` + +2. In another terminal, start Cloudflare Tunnel: + ```bash + cloudflared tunnel --url http://localhost:8080 + ``` + +3. Configure GitHub webhook with the tunnel URL + +4. Test with Claude Code using the prompts + +## Exercises + +### Exercise 1: Custom Workflow Prompt +Create a new prompt that helps with PR reviews by combining: +- Code changes from Module 1 tools +- CI/CD status from Module 2 tools +- A checklist format for reviewers + +### Exercise 2: Event Filtering +Enhance `get_workflow_status` to: +- Filter by workflow conclusion (success/failure) +- Group by repository +- Show time since last run + +### Exercise 3: Notification System +Add a tool that: +- Tracks which events have been "seen" +- Highlights new failures +- Suggests which team member to notify + +## Common Issues + +### Webhook Not Receiving Events +- Ensure Cloudflare Tunnel is running +- Check GitHub webhook settings (should show recent deliveries) +- Verify the payload URL includes `/webhook/github` + +### Prompt Not Working +- FastMCP prompts simply return strings +- Make sure your function is decorated with `@mcp.prompt()` + +### Webhook Server Issues +- Ensure webhook_server.py is running in a separate terminal +- Check that port 8080 is free: `lsof -i :8080` +- The events file will be created automatically when first event is received + +## Next Steps + +Excellent work! You've successfully added real-time capabilities to your MCP server. You now have a system that can: + +- **Analyze code changes** (from Module 1) +- **Monitor CI/CD events in real-time** (from this module) +- **Use MCP Prompts** to provide consistent workflow guidance +- **Handle webhook events** through a clean file-based architecture + +### Key achievements in Module 2: +- Built your first webhook integration +- Learned MCP Prompts for workflow standardization +- Created tools that work with real-time data +- Established patterns for event-driven automation + +### What to do next: +1. **Review the solution** in `/projects/unit3/github-actions-integration/solution/` to see different implementation approaches +2. **Experiment with your prompts** - try using them for different types of GitHub events +3. **Test the integration** - combine your Module 1 file analysis tools with Module 2 event monitoring in a single conversation with Claude +4. **Move on to Module 3** - where you'll complete the automation pipeline by adding team notifications through Slack integration + +Module 3 will bring everything together into a complete workflow that your team can actually use! + +### The story continues... +Your monitoring system is working! CodeCraft Studios now catches CI/CD failures in real-time, and the team feels much more confident about their deployments. But next week brings a new challenge: information silos are causing duplicate work and missed opportunities. Module 3 will complete the automation system with intelligent team notifications that keep everyone in the loop. + +## Additional Resources + +- [MCP Prompts Documentation](https://modelcontextprotocol.io/docs/concepts/prompts) +- [GitHub Webhooks Guide](https://docs.github.com/en/developers/webhooks-and-events) +- [Cloudflare Tunnel Documentation](https://developers.cloudflare.com/cloudflare-one/connections/connect-apps) \ No newline at end of file diff --git a/units/ja/unit3/introduction.mdx b/units/ja/unit3/introduction.mdx new file mode 100644 index 0000000..2781a64 --- /dev/null +++ b/units/ja/unit3/introduction.mdx @@ -0,0 +1,85 @@ +# 高度なMCP開発：Claude Code用カスタムワークフローサーバーの構築 + +ユニット3へようこそ！このユニットでは、3つのMCPプリミティブすべてを学習しながら、カスタム開発ワークフローでClaude Codeを強化する実用的なMCPサーバーを構築します。 + +MCPの作成者の声を聞きたい場合は、彼らが作成したビデオをご覧ください： + + + +このビデオでは、Theo Chu、David Soria Parra、Alex AlbertがModel Context Protocol（MCP）について詳しく解説しています。MCPは、AIアプリケーションが外部データやツールと接続する方法を変革している標準です。 + +## 構築するもの + +**PRエージェントワークフローサーバー** - Claude Codeをチーム対応でワークフロー対応にする方法を実証するMCPサーバー： + +- **スマートPR管理**：MCPツールを使用したコード変更に基づく自動PRテンプレート選択 +- **CI/CD監視**：Cloudflare TunnelとスタンダードプロンプトでGitHub Actionsを追跡 +- **チームコミュニケーション**：すべてのMCPプリミティブが連携するSlack通知 + +## 実世界ケーススタディ + +すべての開発チームが直面する実用的なシナリオを実装します： + +**以前**：開発者が手動でPRを作成し、Actionsの完了を待ち、手動で結果を確認し、チームメンバーへの通知を忘れずに行う + +**以後**：ワークフローサーバーに接続されたClaude Codeは、次のことを知的に行えます： +- 変更されたファイルに基づいて適切なPRテンプレートを提案 +- GitHub Actionsの実行を監視し、フォーマットされた要約を提供 +- デプロイの成功/失敗時にSlack経由でチームに自動通知 +- Actionsの結果に基づいてチーム固有のレビュープロセスを開発者にガイド + +## 主な学習成果 + +1. **コアMCPプリミティブ**：実践的な例を通じてツールとプロンプトをマスター +2. **MCPサーバー開発**：適切な構造とエラーハンドリングを持つ機能的なサーバーの構築 +3. **GitHub Actions統合**：Cloudflare Tunnelを使用してWebhookを受信し、CI/CDイベントを処理 +4. **Hugging Face Hubワークフロー**：LLM開発チーム向けの特化したワークフローの作成 +5. **マルチシステム統合**：MCPを通じてGitHub、Slack、Hugging Face Hubを接続 +6. **Claude Code拡張**：Claudeにチームの特定のワークフローを理解させる + +## MCPプリミティブの実践 + +このユニットでは、コアMCPプリミティブの実践的な経験を提供します： + +- **ツール**（モジュール1）：Claudeがファイルを分析してテンプレートを提案するために呼び出せる関数 +- **プロンプト**（モジュール2）：一貫したチームプロセスのための標準化されたワークフロー +- **統合**（モジュール3）：複雑な自動化のためにすべてのプリミティブが連携 + +## モジュール構成 + +1. **モジュール1：MCPサーバーの構築** - PRテンプレート提案用のツールを持つ基本サーバーの作成 +2. **モジュール2：GitHub Actions統合** - Cloudflare Tunnelとプロンプトを使用したCI/CD監視 +3. **モジュール3：Slack通知** - すべてのMCPプリミティブを統合したチームコミュニケーション + +## 前提条件 + +このユニットを開始する前に、以下を確認してください： + +- ユニット1と2の完了 +- GitHub ActionsとWebhookの概念の基本的な理解 +- テスト用のGitHubリポジトリへのアクセス（個人のテストリポジトリでも可） +- Webhook統合を作成できるSlackワークスペース + +### Claude Codeのインストールとセットアップ + +このユニットでは、MCPサーバー統合をテストするためにClaude Codeが必要です。 + + + +**インストール必須：** このユニットでは、AIワークフローとのMCPサーバー統合をテストするためにClaude Codeが必要です。 + + + +**クイックセットアップ：** + +[公式インストールガイド](https://docs.anthropic.com/en/docs/claude-code/getting-started)に従ってClaude Codeをインストールし、認証を完了してください。主要なステップは、npmを介したインストール、プロジェクトディレクトリへの移動、`claude`の実行によるconsole.anthropic.comを通じた認証です。 + +インストール後は、このユニット全体でClaude Codeを使用してMCPサーバーをテストし、構築するワークフロー自動化と相互作用します。 + + + +**Claude Codeが初めてですか？** 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pkN=6oe=dUmcW#D0>z}4E4TZbcrxmo|egOZRIC|zt>fvAi_&?7aFbV(w literal 0 HcmV?d00001 diff --git a/units/ja/unit3/slack-notification.mdx b/units/ja/unit3/slack-notification.mdx new file mode 100644 index 0000000..92fbc6b --- /dev/null +++ b/units/ja/unit3/slack-notification.mdx @@ -0,0 +1,376 @@ +# Module 3: Slack Notification + +## The Communication Gap Crisis + +Week 3 at CodeCraft Studios. Your automation system is already transforming how the team works: +- **PR Agent** (Module 1): Developers are writing clear, helpful pull request descriptions +- **CI/CD Monitor** (Module 2): The team catches test failures immediately, preventing bugs from reaching production + +The team is feeling much more confident... until Monday morning brings a new crisis. + +The frontend team (Emma and Jake) spent the entire weekend debugging a nasty API integration issue. They tried everything: checked their network calls, validated request formats, even rewrote the error handling. Finally, at 2 AM Sunday, they discovered the backend team had fixed this exact issue on Friday and deployed the fix to staging - but forgot to announce it. + +"We wasted 12 hours solving a problem that was already fixed!" Emma says, frustrated. + +Meanwhile, the design team finished the new user onboarding flow illustrations last week, but the frontend team didn't know they were ready. Those beautiful assets are still sitting unused while the team ships a temporary design. + +The team realizes they have an information silo problem. Everyone's working hard, but they're not communicating effectively about what's happening when. + +**Your mission**: Complete the automation system with intelligent Slack notifications that keep the whole team informed about important developments automatically. + +## What You'll Build + +This final module completes the CodeCraft Studios transformation. You'll integrate Tools and Prompts to create a smart notification system that sends formatted Slack messages about CI/CD events, demonstrating how all MCP primitives work together in a real-world scenario. + +Building on the foundation from Modules 1 and 2, you'll add the final piece of the puzzle: +- **Slack webhook tool** for sending messages to your team channel +- **Two notification prompts** that intelligently format CI events +- **Complete integration** showing all MCP primitives working together + +### Screencast: The Complete Automation System! 🎉 + + + +**The Final Piece**: Watch how your complete automation system prevents those Monday morning surprises that plagued Emma and Jake! + +**What You'll See**: +- **Claude's intelligent workflow** - Notice how Claude breaks down the task: ☐ Check events → ☐ Send notification +- **Real-time MCP tools in action** - `get_recent_actions_events` pulls fresh CI data, then `send_slack_notification` delivers the alert +- **Side-by-side demonstration** - The Slack channel is open in parallel to show the formatted message appearing as Claude sends it + +**The Smart Notification**: Claude doesn't just spam the team—it crafts a professional alert with: +- 🚨 Clear urgency indicators and emoji +- **Detailed failure breakdown** (test-auth-service ❌, test-api ❌, test-frontend ⏳) +- **Actionable links** to the pipeline run and pull request +- **Context everyone needs** - repository, PR #1 "various improvements", commit hash + +**Why This Matters**: Remember the communication gap crisis? No more! This system ensures that when CI fails on `demo-bad-pr` branch, the whole team knows immediately. No more weekend debugging sessions for issues that were already fixed! + +**The Complete Journey**: From Module 1's PR chaos to Module 3's intelligent team notifications—you've built a system that transforms how CodeCraft Studios collaborates. The weekend warriors become informed teammates! 🚀 + +## Learning Objectives + +By the end of this module, you'll understand: +1. How to integrate external APIs with MCP Tools +2. How to combine Tools and Prompts for complete workflows +3. How to format rich messages using Slack markdown +4. How all MCP primitives work together in practice + +## Prerequisites + +You'll need everything from the previous modules plus: +- **Completed Modules 1 and 2** - This module directly extends your existing MCP server +- **A Slack workspace** where you can create incoming webhooks (personal workspaces work fine) +- **Basic understanding of REST APIs** - You'll be making HTTP requests to Slack's webhook endpoints + +## Key Concepts + +### MCP Integration Pattern + +This module demonstrates the complete workflow: +1. **Events** → GitHub Actions webhook (from Module 2) +2. **Prompts** → Format events into readable messages +3. **Tools** → Send formatted messages to Slack +4. **Result** → Professional team notifications + +### Slack Markdown Formatting + +You'll use [Slack's markdown](https://api.slack.com/reference/surfaces/formatting) for rich messages: +- [`*bold text*`](https://api.slack.com/reference/surfaces/formatting#visual-styles) for emphasis +- [`_italic text_`](https://api.slack.com/reference/surfaces/formatting#visual-styles) for details +- [`` `code blocks` ``](https://api.slack.com/reference/surfaces/formatting#inline-code) for technical info +- [`> quoted text`](https://api.slack.com/reference/surfaces/formatting#quotes) for summaries +- [Emoji](https://api.slack.com/reference/surfaces/formatting#emoji): ✅ ❌ 🚀 ⚠️ +- [Links](https://api.slack.com/reference/surfaces/formatting#linking-urls): `` + +## Project Structure + +``` +slack-notification/ +├── starter/ # Your starting point +│ ├── server.py # Modules 1+2 code + TODOs +│ ├── webhook_server.py # From Module 2 +│ ├── pyproject.toml +│ └── README.md +└── solution/ # Complete implementation + ├── server.py # Full Slack integration + ├── webhook_server.py + └── README.md +``` + +## Implementation Steps + +### Step 1: Set Up Slack Integration (10 min) + +1. Create a Slack webhook: + - Go to [Slack API Apps](https://api.slack.com/apps) + - Create new app → "From scratch" ([Creating an app guide](https://api.slack.com/authentication/basics#creating)) + - App Name: "MCP Course Notifications" + - Choose your workspace + - Go to "Features" → "[Incoming Webhooks](https://api.slack.com/messaging/webhooks)" + - [Activate incoming webhooks](https://api.slack.com/messaging/webhooks#enable_webhooks) + - Click "Add New Webhook to Workspace" + - Choose channel and authorize ([Webhook setup guide](https://api.slack.com/messaging/webhooks#getting_started)) + - Copy the webhook URL + +2. Test webhook works (following [webhook posting examples](https://api.slack.com/messaging/webhooks#posting_with_webhooks)): + ```bash + curl -X POST -H 'Content-type: application/json' \ + --data '{"text":"Hello from MCP Course!"}' \ + YOUR_WEBHOOK_URL + ``` + +3. Set environment variable: + ```bash + export SLACK_WEBHOOK_URL="https://hooks.slack.com/services/YOUR/WEBHOOK/URL" + ``` + + **⚠️ Security Note**: The webhook URL is a sensitive secret that grants permission to post messages to your Slack channel. Always: + - Store it as an environment variable, never hardcode it in your code + - Never commit webhook URLs to version control (add to .gitignore) + - Treat it like a password - anyone with this URL can send messages to your channel + + + +**Security Alert**: Webhook URLs are sensitive credentials! Anyone with your webhook URL can send messages to your Slack channel. Always store them as environment variables and never commit them to version control. + + + +### Step 2: Add Slack Tool (15 min) + +Now that you have a working webhook, you'll add a new MCP tool to your existing server.py from Module 2. This tool will handle sending notifications to Slack by making HTTP requests to the webhook URL. + + + +**Note**: The starter code includes all improvements from Modules 1 & 2 (output limiting, webhook handling). Focus on the new Slack integration! + + + +Add this tool to your server.py: + +**`send_slack_notification`**: +- Takes a message string parameter +- Reads webhook URL from environment variable +- Sends POST request to Slack webhook +- Returns success/failure message +- Handles basic error cases + +```python +import os +import requests +from mcp.types import TextContent + +@mcp.tool() +def send_slack_notification(message: str) -> str: + """Send a formatted notification to the team Slack channel.""" + webhook_url = os.getenv("SLACK_WEBHOOK_URL") + if not webhook_url: + return "Error: SLACK_WEBHOOK_URL environment variable not set" + + try: + # TODO: Send POST request to webhook_url + # TODO: Include message in JSON payload with "mrkdwn": true + # TODO: Handle response and return status + pass + except Exception as e: + return f"Error sending message: {str(e)}" +``` + +### Step 3: Create Formatting Prompts (15 min) + +Next, you'll add MCP Prompts to your server - this is where the magic happens! These prompts will work with Claude to automatically format your GitHub webhook data into well-structured Slack messages. Remember from Module 1 that Prompts provide reusable instructions that Claude can use consistently. + +Implement two prompts that generate Slack-formatted messages: + +1. **`format_ci_failure_alert`**: + ```python + @mcp.prompt() + def format_ci_failure_alert() -> str: + """Create a Slack alert for CI/CD failures.""" + return """Format this GitHub Actions failure as a Slack message: + + Use this template: + :rotating_light: *CI Failure Alert* :rotating_light: + + A CI workflow has failed: + *Workflow*: workflow_name + *Branch*: branch_name + *Status*: Failed + *View Details*: + + Please check the logs and address any issues. + + Use Slack markdown formatting and keep it concise for quick team scanning.""" + ``` + +2. **`format_ci_success_summary`**: + ```python + @mcp.prompt() + def format_ci_success_summary() -> str: + """Create a Slack message celebrating successful deployments.""" + return """Format this successful GitHub Actions run as a Slack message: + + Use this template: + :white_check_mark: *Deployment Successful* :white_check_mark: + + Deployment completed successfully for [Repository Name] + + *Changes:* + - Key feature or fix 1 + - Key feature or fix 2 + + *Links:* + + + Keep it celebratory but informative. Use Slack markdown formatting.""" + ``` + +### Step 4: Test Complete Workflow (10 min) + +Now comes the exciting part - testing your complete MCP workflow! You'll have all three components working together: webhook capture from Module 2, prompt formatting from this module, and Slack notifications. + +1. Start all services (just like in Module 2, but now with Slack integration): + ```bash + # Terminal 1: Start webhook server + python webhook_server.py + + # Terminal 2: Start MCP server + uv run server.py + + # Terminal 3: Start Cloudflare Tunnel + cloudflared tunnel --url http://localhost:8080 + ``` + +2. Test the complete integration with Claude Code: + - **Configure GitHub webhook** with tunnel URL (same as Module 2) + - **Push changes** to trigger GitHub Actions + - **Ask Claude** to check recent events and format them using your prompts + - **Let Claude send** the formatted message using your Slack tool + - **Verify** notifications appear in your Slack channel + +### Step 5: Verify Integration (5 min) + +You can test your implementation without setting up a real GitHub repository! See `manual_test.md` for curl commands that simulate GitHub webhook events. + +**Understanding the webhook event flow:** +- Your webhook server (from Module 2) captures GitHub events and stores them in `github_events.json` +- Your MCP tools read from this file to get recent CI/CD activity +- Claude uses your formatting prompts to create readable messages +- Your Slack tool sends the formatted messages to your team channel +- This creates a complete pipeline: GitHub → Local Storage → Claude Analysis → Slack Notification + +**Quick Test Workflow:** +1. Use curl to send fake GitHub events to your webhook server +2. Ask Claude to check recent events and format them +3. Send formatted messages to Slack +4. Verify everything works end-to-end + +**Manual Testing Alternative:** For a complete testing experience without GitHub setup, follow the step-by-step curl commands in `manual_test.md`. + +## Example Workflow in Claude Code + +``` +User: "Check recent CI events and notify the team about any failures" + +Claude: +1. Uses get_recent_actions_events (from Module 2) +2. Finds a workflow failure +3. Uses format_ci_failure_alert prompt to create message +4. Uses send_slack_notification tool to deliver it +5. Reports back: "Sent failure alert to #dev-team channel" +``` + +## Expected Slack Message Output + +**Failure Alert:** +``` +🚨 *CI Failure Alert* 🚨 + +A CI workflow has failed: +*Workflow*: CI (Run #42) +*Branch*: feature/slack-integration +*Status*: Failed +*View Details*: + +Please check the logs and address any issues. +``` + +**Success Summary:** +``` +✅ *Deployment Successful* ✅ + +Deployment completed successfully for mcp-course + +*Changes:* +- Added team notification system +- Integrated MCP Tools and Prompts + +*Links:* + +``` + +## Common Issues + +### Webhook URL Issues +- Verify the environment variable is set correctly +- Test webhook directly with curl before integrating +- Ensure Slack app has proper permissions + +### Message Formatting +- [Slack markdown](https://api.slack.com/reference/surfaces/formatting) differs from GitHub markdown +- **Important**: Use `*text*` for bold (not `**text**`) +- Include `"mrkdwn": true` in webhook payload for proper formatting +- Test message formatting manually before automating +- Handle special characters in commit messages properly ([formatting reference](https://api.slack.com/reference/surfaces/formatting#escaping)) + +### Network Errors +- Add basic timeout handling to webhook requests ([webhook error handling](https://api.slack.com/messaging/webhooks#handling_errors)) +- Return meaningful error messages from the tool +- Check internet connectivity if requests fail + +## Key Takeaways + +You've now built a complete MCP workflow that demonstrates: +- **Tools** for external API integration (Slack webhooks) +- **Prompts** for intelligent message formatting +- **Integration** of all MCP primitives working together +- **Real-world application** that teams can actually use + +This shows the power of MCP for building practical development automation tools! + + + +**Key Learning**: You've now built a complete MCP workflow that combines Tools (for external API calls) with Prompts (for consistent formatting). This pattern of Tools + Prompts is fundamental to advanced MCP development and can be applied to many other automation scenarios. + + + +## Next Steps + +Congratulations! You've completed the final module of Unit 3 and built a complete end-to-end automation system. Your journey through all three modules has given you hands-on experience with: + +- **Module 1**: MCP Tools and intelligent data analysis +- **Module 2**: Real-time webhooks and MCP Prompts +- **Module 3**: External API integration and workflow completion + +### What to do next: +1. **Test your complete system** - Try triggering real GitHub events and watch the full pipeline work +2. **Experiment with customization** - Modify the Slack message formats or add new notification types +3. **Review the Unit 3 Conclusion** - Reflect on everything you've learned and explore next steps +4. **Share your success** - Show teammates how MCP can automate your development workflows + +You now have a solid foundation for building intelligent automation systems with MCP! + +### The transformation is complete! +CodeCraft Studios has gone from chaotic development to a well-oiled machine. The automation system you built handles: +- **Smart PR descriptions** that help reviewers understand changes +- **Real-time CI/CD monitoring** that catches failures before they reach production +- **Intelligent team notifications** that keep everyone informed automatically + +The team can now focus on building great products instead of fighting process problems. And you've learned advanced MCP patterns that you can apply to any automation challenge! + +## Additional Resources + +- [Slack Incoming Webhooks Documentation](https://api.slack.com/messaging/webhooks) +- [Slack Message Formatting Guide](https://api.slack.com/reference/surfaces/formatting) +- [MCP Tools Documentation](https://modelcontextprotocol.io/docs/concepts/tools) +- [MCP Prompts Guide](https://modelcontextprotocol.io/docs/concepts/prompts) \ No newline at end of file diff --git a/units/ja/unit3_1/conclusion.mdx b/units/ja/unit3_1/conclusion.mdx new file mode 100644 index 0000000..2c93124 --- /dev/null +++ b/units/ja/unit3_1/conclusion.mdx @@ -0,0 +1,42 @@ +# Conclusion + +Congratulations! 🎉 You've successfully built a Pull Request Agent that automatically enhances Hugging Face model repositories through intelligent tagging using MCP (Model Context Protocol). + +The patterns you've learned - webhook processing, MCP tool integration, agent orchestration, and production deployment - are foundational skills for agent and MCP building. These techniques are applicable far beyond model tagging and represent a powerful approach to building intelligent systems that augment human capabilities. + +## What we've built + +Throughout this unit, you created a complete automation system with four key components: + +- **MCP Server** (`mcp_server.py`) - FastMCP-based server with Hub API integration +- **MCP Client** (Agent) - Intelligent orchestration with language model reasoning +- **Webhook Listener** (FastAPI) - Real-time event processing from Hugging Face Hub +- **Testing Interface** (Gradio) - Development and monitoring dashboard + +## Next Steps + +### Continue Learning +- Explore advanced MCP patterns and tools +- Study other automation frameworks and AI system architecture +- Learn about multi-agent systems and tool composition + +### Build More Agents +- Develop domain-specific automation tools for your own projects +- Try out other types of webhooks (e.g. model uploads, model downloads, etc.) +- Experiment with different workflows + +### Share Your Work +- Open source your agent for the community +- Write about your learnings and automation patterns +- Contribute to the MCP ecosystem + +### Scale Your Impact +- Deploy agents for multiple repositories or organizations +- Build more sophisticated automation workflows +- Explore commercial applications of AI automation + + + +Consider documenting your experience and sharing it with the community! Your journey from learning MCP to building a production agent will help others explore AI automation. + + diff --git a/units/ja/unit3_1/creating-the-mcp-server.mdx b/units/ja/unit3_1/creating-the-mcp-server.mdx new file mode 100644 index 0000000..9ab67e0 --- /dev/null +++ b/units/ja/unit3_1/creating-the-mcp-server.mdx @@ -0,0 +1,336 @@ +# Creating the MCP Server + +The MCP server is the heart of our Pull Request Agent. It provides the tools that our agent will use to interact with the Hugging Face Hub, specifically for reading and updating model repository tags. In this section, we'll build the server using FastMCP and the Hugging Face Hub Python SDK. + +## Understanding the MCP Server Architecture + +Our MCP server provides two essential tools: + +| Tool | Description | +| --- | --- | +| `get_current_tags` | Retrieves existing tags from a model repository | +| `add_new_tag` | Adds a new tag to a repository via pull request | + +These tools abstract the complexity of Hub API interactions and provide a clean interface for our agent to work with. + +![MCP Server Tools](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit3/architecture.png) + +## Complete MCP Server Implementation + +Let's create our `mcp_server.py` file step by step. We'll build this incrementally so you understand each component and how they work together. + +### 1. Imports and Configuration + +First, let's set up all the necessary imports and configuration. + +```python +#!/usr/bin/env python3 +""" +Simplified MCP Server for HuggingFace Hub Tagging Operations using FastMCP +""" + +import os +import json +from fastmcp import FastMCP +from huggingface_hub import HfApi, model_info, ModelCard, ModelCardData +from huggingface_hub.utils import HfHubHTTPError +from dotenv import load_dotenv + +load_dotenv() +``` + +The imports above give us everything we need to build our MCP server. `FastMCP` provides the server framework, while the `huggingface_hub` imports give us the tools to interact with model repositories. + +The `load_dotenv()` call automatically loads environment variables from a `.env` file, making it easy to manage secrets like API tokens during development. + + + +If you're using uv, you can create a `.env` file in the root of the project and you won't need to use `load_dotenv()` if you use `uv run` to run the server. + + + +Next, we'll configure our server with the necessary credentials and create the FastMCP instance: + +```python +# Configuration +HF_TOKEN = os.getenv("HF_TOKEN") + +# Initialize HF API client +hf_api = HfApi(token=HF_TOKEN) if HF_TOKEN else None + +# Create the FastMCP server +mcp = FastMCP("hf-tagging-bot") +``` + +This configuration block does three important things: +1. Retrieves the Hugging Face token from environment variables +2. Creates an authenticated API client (only if a token is available) +3. Initializes our FastMCP server with a descriptive name + +The conditional creation of `hf_api` ensures our server can start even without a token, which is useful for testing the basic structure. + +### 2. Get Current Tags Tool + +Now let's implement our first tool - `get_current_tags`. This tool retrieves the existing tags from a model repository: + +```python +@mcp.tool() +def get_current_tags(repo_id: str) -> str: + """Get current tags from a HuggingFace model repository""" + print(f"🔧 get_current_tags called with repo_id: {repo_id}") + + if not hf_api: + error_result = {"error": "HF token not configured"} + json_str = json.dumps(error_result) + print(f"❌ No HF API token - returning: {json_str}") + return json_str +``` + +The function starts with validation - checking if we have an authenticated API client. Notice how we return JSON strings instead of Python objects. This is crucial for MCP communication. + + + +All MCP tools must return strings, not Python objects. That's why we use `json.dumps()` to convert our results to JSON strings. This ensures reliable data exchange between the MCP server and client. + + + +Let's continue with the main logic of the `get_current_tags` function: + +```python + try: + print(f"📡 Fetching model info for: {repo_id}") + info = model_info(repo_id=repo_id, token=HF_TOKEN) + current_tags = info.tags if info.tags else [] + print(f"🏷️ Found {len(current_tags)} tags: {current_tags}") + + result = { + "status": "success", + "repo_id": repo_id, + "current_tags": current_tags, + "count": len(current_tags), + } + json_str = json.dumps(result) + print(f"✅ get_current_tags returning: {json_str}") + return json_str + + except Exception as e: + print(f"❌ Error in get_current_tags: {str(e)}") + error_result = {"status": "error", "repo_id": repo_id, "error": str(e)} + json_str = json.dumps(error_result) + print(f"❌ get_current_tags error returning: {json_str}") + return json_str +``` + +This implementation follows a clear pattern: +1. **Fetch data** using the Hugging Face Hub API +2. **Process the response** to extract tag information +3. **Structure the result** in a consistent JSON format +4. **Handle errors gracefully** with detailed error messages + + + +The extensive logging might seem overkill, but it helps with debugging and monitoring when the server is running. Remember, your application will autonomously reacting to events from the Hub, so you won't be able to see the logs in real time. + + + +### 3. Add New Tag Tool + +Now for the more complex tool - `add_new_tag`. This tool adds a new tag to a repository by creating a pull request. Let's start with the initial setup and validation: + +```python +@mcp.tool() +def add_new_tag(repo_id: str, new_tag: str) -> str: + """Add a new tag to a HuggingFace model repository via PR""" + print(f"🔧 add_new_tag called with repo_id: {repo_id}, new_tag: {new_tag}") + + if not hf_api: + error_result = {"error": "HF token not configured"} + json_str = json.dumps(error_result) + print(f"❌ No HF API token - returning: {json_str}") + return json_str +``` + +Similar to our first tool, we start with validation. Now let's fetch the current repository state to check if the tag already exists: + +```python + try: + # Get current model info and tags + print(f"📡 Fetching current model info for: {repo_id}") + info = model_info(repo_id=repo_id, token=HF_TOKEN) + current_tags = info.tags if info.tags else [] + print(f"🏷️ Current tags: {current_tags}") + + # Check if tag already exists + if new_tag in current_tags: + print(f"⚠️ Tag '{new_tag}' already exists in {current_tags}") + result = { + "status": "already_exists", + "repo_id": repo_id, + "tag": new_tag, + "message": f"Tag '{new_tag}' already exists", + } + json_str = json.dumps(result) + print(f"🏷️ add_new_tag (already exists) returning: {json_str}") + return json_str +``` + +This section demonstrates an important principle: **validate before acting**. We check if the tag already exists to avoid creating unnecessary pull requests. + + + +Always check the current state before making changes. This prevents duplicate work and provides better user feedback. It's especially important when creating pull requests, as duplicate PRs can clutter the repository. + + + +Next, we'll prepare the updated tag list and handle the model card: + +```python + # Add the new tag to existing tags + updated_tags = current_tags + [new_tag] + print(f"🆕 Will update tags from {current_tags} to {updated_tags}") + + # Create model card content with updated tags + try: + # Load existing model card + print(f"📄 Loading existing model card...") + card = ModelCard.load(repo_id, token=HF_TOKEN) + if not hasattr(card, "data") or card.data is None: + card.data = ModelCardData() + except HfHubHTTPError: + # Create new model card if none exists + print(f"📄 Creating new model card (none exists)") + card = ModelCard("") + card.data = ModelCardData() + + # Update tags - create new ModelCardData with updated tags + card_dict = card.data.to_dict() + card_dict["tags"] = updated_tags + card.data = ModelCardData(**card_dict) +``` + +This section handles model card management. We try to load an existing model card first, but create a new one if none exists. This ensures our tool works with any repository, even if it's empty. + +The model card (`README.md`) contains the repository metadata, including tags. By updating the model card data and creating a pull request, we're following the standard Hugging Face workflow for metadata changes. + +Now for the pull request creation - the main part of our tool: + +```python + # Create a pull request with the updated model card + pr_title = f"Add '{new_tag}' tag" + pr_description = f""" +## Add tag: {new_tag} + +This PR adds the `{new_tag}` tag to the model repository. + +**Changes:** +- Added `{new_tag}` to model tags +- Updated from {len(current_tags)} to {len(updated_tags)} tags + +**Current tags:** {", ".join(current_tags) if current_tags else "None"} +**New tags:** {", ".join(updated_tags)} + +🤖 This is a pull request created by the Hugging Face Hub Tagging Bot. +""" + + print(f"🚀 Creating PR with title: {pr_title}") +``` + +We create a detailed pull request description that explains what's changing and why. This transparency is crucial for repository maintainers who will review the PR. + + + +Clear, detailed PR descriptions are essential for automated pull requests. They help repository maintainers understand what's happening and make informed decisions about whether to merge the changes. + +Also, it's good practice to clearly state that the PR is created by an automated tool. This helps repository maintainers understand how to deal with the PR. + + + +Finally, we create the commit and pull request: + +```python + # Create commit with updated model card using CommitOperationAdd + from huggingface_hub import CommitOperationAdd + + commit_info = hf_api.create_commit( + repo_id=repo_id, + operations=[ + CommitOperationAdd( + path_in_repo="README.md", path_or_fileobj=str(card).encode("utf-8") + ) + ], + commit_message=pr_title, + commit_description=pr_description, + token=HF_TOKEN, + create_pr=True, + ) + + # Extract PR URL from commit info + pr_url_attr = commit_info.pr_url + pr_url = pr_url_attr if hasattr(commit_info, "pr_url") else str(commit_info) + + print(f"✅ PR created successfully! URL: {pr_url}") + + result = { + "status": "success", + "repo_id": repo_id, + "tag": new_tag, + "pr_url": pr_url, + "previous_tags": current_tags, + "new_tags": updated_tags, + "message": f"Created PR to add tag '{new_tag}'", + } + json_str = json.dumps(result) + print(f"✅ add_new_tag success returning: {json_str}") + return json_str +``` + +The `create_commit` function with `create_pr=True` is the key to our automation. It creates a commit with the updated `README.md` file and automatically opens a pull request for review. + +Don't forget the error handling for this complex operation: + +```python + except Exception as e: + print(f"❌ Error in add_new_tag: {str(e)}") + print(f"❌ Error type: {type(e)}") + import traceback + print(f"❌ Traceback: {traceback.format_exc()}") + + error_result = { + "status": "error", + "repo_id": repo_id, + "tag": new_tag, + "error": str(e), + } + json_str = json.dumps(error_result) + print(f"❌ add_new_tag error returning: {json_str}") + return json_str +``` + +The comprehensive error handling includes the full traceback, which is invaluable for debugging when things go wrong. + +Emojis in log messages might seem silly, but they make scanning logs much faster. 🔧 for function calls, 📡 for API requests, ✅ for success, and ❌ for errors create visual patterns that help you quickly find what you're looking for. + + + +Whilst building this application, it's easy to accidentally create an infinite loop of PRs. This is because the `create_commit` function with `create_pr=True` will create a PR for every commit. If the PR is not merged, the `create_commit` function will be called again, and again, and again... + +We've added checks to prevent this, but it's something to be aware of. + + + +## Next Steps + +Now that we have our MCP server implemented with robust tagging tools, we need to: + +1. **Create the MCP Client** - Build the interface between our agent and MCP server +2. **Implement Webhook Handling** - Listen for Hub discussion events +3. **Integrate Agent Logic** - Connect webhooks with MCP tool calls +4. **Test the Complete System** - Validate end-to-end functionality + +In the next section, we'll create the MCP client that will allow our webhook handler to interact with these tools intelligently. + + + +The MCP server runs as a separate process from your main application. This isolation provides better error handling and allows the server to be reused by multiple clients or applications. + + \ No newline at end of file diff --git a/units/ja/unit3_1/introduction.mdx b/units/ja/unit3_1/introduction.mdx new file mode 100644 index 0000000..075129d --- /dev/null +++ b/units/ja/unit3_1/introduction.mdx @@ -0,0 +1,108 @@ +# Build a Pull Request Agent on the Hugging Face Hub + +Welcome to Unit 3 of the MCP Course! + +In this unit, we'll build a pull request agent that automatically tags Hugging Face model repositories based on discussions and comments. This real-world application demonstrates how to integrate MCP with webhook listeners and automated workflows. + + + +This unit showcases a real world use case where MCP servers can respond to real-time events from the Hugging Face Hub, automatically creating pull requests to improve repository metadata. + + + +## What You'll Learn + +In this unit, you will: + +- Create an MCP Server that interacts with the Hugging Face Hub API +- Implement webhook listeners to respond to discussion events +- Set up automated tagging workflows for model repositories +- Deploy a complete webhook-driven application to Hugging Face Spaces + +By the end of this unit, you'll have a working PR agent that can monitor discussions and automatically improve repository metadata through pull requests. + +## Prerequisites + +Before proceeding with this unit, make sure you: + +- Have completed Units 1 and 2, or have experience with MCP concepts +- Are comfortable with Python, FastAPI, and webhook concepts +- Have a basic understanding of Hugging Face Hub workflows and pull requests +- Have a development environment with: + - Python 3.11+ + - A Hugging Face account with API access + +## Our Pull Request Agent Project + +We'll build a tagging agent that consists of four main components: the MCP server, webhook listener, agent logic, and deployment infrastructure. The agent will be able to tag model repositories based on discussions and comments. This should save model authors time by receiving ready to use PRs, instead of having to manually tag their repositories. + +![PR Agent Architecture](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit3/architecture.png) + +In the diagram above, we have a MCP server that can read and update model tags. We have a webhook listener that can receive webhooks from the Hugging Face Hub. We have an agent that can analyze discussions and comments and create PRs to update model tags. We have a deployment infrastructure that can deploy the MCP server to Hugging Face Spaces. + +### Project Overview + + To build this application we will need the following files: + +| File | Purpose | Description | +|------|---------|-------------| +| `mcp_server.py` | **Core MCP Server** | FastMCP-based server with tools for reading and updating model tags | +| `app.py` | **Webhook Listener & Agent** | FastAPI app that receives webhooks, processes discussions, and creates PRs | +| `requirements.txt` | **Dependencies** | Python packages including FastMCP, FastAPI, and huggingface-hub | +| `pyproject.toml` | **Project Configuration** | Modern Python packaging with uv dependency management | +| `Dockerfile` | **Deployment** | Container configuration for Hugging Face Spaces | +| `env.example` | **Configuration Template** | Required environment variables and secrets | +| `cleanup.py` | **Utility** | Helper script for development and testing cleanup | + +Let's go through each of these files and understand their purpose. + +### MCP Server (`mcp_server.py`) + +The heart of our application - a FastMCP server that provides tools for: +- Reading current tags from model repositories +- Adding new tags via pull requests to the Hub +- Error handling and validation + +This is where you will implement the MCP server and do most of the work for this project. The Gradio app and FastAPI app will be used to test the MCP server and the webhook listener, and they are ready to use. + +### Webhook Integration + +Following the [Hugging Face Webhooks Guide](https://huggingface.co/docs/hub/webhooks-guide-discussion-bot), our agent: +- Listens for discussion comment events +- Validates webhook signatures for security +- Processes mentions and tag suggestions +- Creates pull requests automatically + +### Agent Functionality + +The agent analyzes discussion content to: +- Extract explicit tag mentions (`tag: pytorch`, `#transformers`) +- Recognize implicit tags from natural language +- Validate tags against known ML/AI categories +- Generate appropriate pull request descriptions + +### Deployment & Production + +- Containerized deployment to Hugging Face Spaces +- Environment variable management for secrets +- Background task processing for webhook responses +- Gradio interface for testing and monitoring + +## Webhook Integration Overview + +Our PR agent leverages the same webhook infrastructure used by Hugging Face's discussion bots. Here's how webhooks enable real-time responses: + +![Webhook Flow](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/webhook-creation.png) + +The webhook flow works as follows: +1. **Event Trigger**: A user creates a comment in a model repository discussion +2. **Webhook Delivery**: Hugging Face sends a POST request to our endpoint +3. **Authentication**: We validate the webhook secret for security +4. **Processing**: Our agent analyzes the comment for tag suggestions +5. **Action**: If relevant tags are found, we create a pull request +6. **Response**: The webhook returns immediately while PR creation happens in the background + +## Let's Get Started! + +Ready to build a production-ready PR agent that can automatically improve Hugging Face repositories? Let's begin by setting up the project structure and understanding the MCP server implementation. + diff --git a/units/ja/unit3_1/mcp-client.mdx b/units/ja/unit3_1/mcp-client.mdx new file mode 100644 index 0000000..dd517a6 --- /dev/null +++ b/units/ja/unit3_1/mcp-client.mdx @@ -0,0 +1,370 @@ +# MCP Client + +Now that we have our MCP server with tagging tools, we need to create a client that can interact with these tools. The MCP client serves as the bridge between our webhook handler and the MCP server, enabling our agent to use the Hub tagging functionality. + +For the sake of this project, we'll build both an API and a Gradio app. The API will be used to test the MCP server and the webhook listener, and the Gradio app will be used to test the MCP client with simulated webhook events. + + + +For educational purposes, we will build the MCP Server and MCP Client in the same repo. In a real-world application, you would likely have a separate repo for the MCP Server and MCP Client. In fact, you might only build one of these components. + + + +## Understanding the MCP Client Architecture + +In our application, the MCP client is integrated into the main FastAPI application (`app.py`). It creates and manages connections to our MCP server, providing a seamless interface for tool execution. + +![MCP Client Integration](https://huggingface.co/datasets/mcp-course/images/resolve/main/unit3/app.png) + +## Agent-Based MCP Client + +We use the `huggingface_hub` Agent class that has built-in MCP support. This provides both language model capabilities and MCP tool integration in a single component. + +### 1. Agent Configuration + +Let's start by setting up the agent configuration and understanding each component: + +```python +from huggingface_hub.inference._mcp.agent import Agent +from typing import Optional, Literal + +# Configuration +HF_TOKEN = os.getenv("HF_TOKEN") +HF_MODEL = os.getenv("HF_MODEL", "microsoft/DialoGPT-medium") +DEFAULT_PROVIDER: Literal["hf-inference"] = "hf-inference" + +# Global agent instance +agent_instance: Optional[Agent] = None +``` + +We start with the necessary imports and configuration. The global `agent_instance` variable ensures we create the agent only once and reuse it across multiple requests. This is important for performance since agent initialization can be expensive. + +Now let's implement the function that creates and manages our agent: + +```python +async def get_agent(): + """Get or create Agent instance""" + print("🤖 get_agent() called...") + global agent_instance + if agent_instance is None and HF_TOKEN: + print("🔧 Creating new Agent instance...") + print(f"🔑 HF_TOKEN present: {bool(HF_TOKEN)}") + print(f"🤖 Model: {HF_MODEL}") + print(f"🔗 Provider: {DEFAULT_PROVIDER}") +``` + +The function starts by checking if we already have an agent instance. This singleton pattern prevents unnecessary recreations and ensures consistent state. + +Let's continue with the agent creation: + +```python + try: + agent_instance = Agent( + model=HF_MODEL, + provider=DEFAULT_PROVIDER, + api_key=HF_TOKEN, + servers=[ + { + "type": "stdio", + "config": { + "command": "python", + "args": ["mcp_server.py"], + "cwd": ".", + "env": {"HF_TOKEN": HF_TOKEN} if HF_TOKEN else {}, + }, + } + ], + ) + print("✅ Agent instance created successfully") + print("🔧 Loading tools...") + await agent_instance.load_tools() + print("✅ Tools loaded successfully") + except Exception as e: + print(f"❌ Error creating/loading agent: {str(e)}") + agent_instance = None +``` + +This is where the important part happens! Let's break down the Agent configuration: + +**Agent Parameters:** +- `model`: The language model that will reason about tool usage +- `provider`: How to access the model (Hugging Face Inference Providers) +- `api_key`: Hugging Face API key + +**MCP Server Connection:** +- `type: "stdio"`: Connect to the MCP server via standard input/output +- `command: "python"`: Run our MCP server as a Python subprocess +- `args: ["mcp_server.py"]`: The script file to execute +- `env`: Pass the HF_TOKEN to the server process + + + +The `stdio` connection type means the agent starts your MCP server as a subprocess and communicates with it through standard input/output. This is perfect for development and single-machine deployments. + + + +The `load_tools()` call is crucial - it discovers what tools are available from the MCP server and makes them accessible to the agent's reasoning engine. + +This completes our agent management function with proper error handling and logging. + +## Tool Discovery and Usage + +Once the agent is created and tools are loaded, it can automatically discover and use the MCP tools. This is where the real power of the Agent approach shines. + +### Available Tools + +The agent discovers our MCP tools automatically: +- `get_current_tags(repo_id: str)` - Retrieve existing repository tags +- `add_new_tag(repo_id: str, new_tag: str)` - Add new tag via pull request + +The agent doesn't just call these tools blindly - it reasons about when and how to use them based on the prompt you give it. + +### Tool Execution Example + +Here's how the agent intelligently uses tools: + +```python +# Example of how the agent would use tools +async def example_tool_usage(): + agent = await get_agent() + + if agent: + # The agent can reason about which tools to use + response = await agent.run( + "Check the current tags for microsoft/DialoGPT-medium and add the tag 'conversational-ai' if it's not already present" + ) + print(response) +``` + +Notice how we give the agent a natural language instruction, and it figures out: +1. First call `get_current_tags` to see what tags exist +2. Check if `conversational-ai` is already there +3. If not, call `add_new_tag` to add it +4. Provide a summary of what it did + +This is much more intelligent than calling tools directly! + +## Integration with Webhook Processing + +Now let's see how the MCP client integrates into our webhook processing pipeline. This is where everything comes together. + +### 1. Tag Extraction and Processing + +Here's the main function that processes webhook events and uses our MCP agent: + +```python +async def process_webhook_comment(webhook_data: Dict[str, Any]): + """Process webhook to detect and add tags""" + print("🏷️ Starting process_webhook_comment...") + + try: + comment_content = webhook_data["comment"]["content"] + discussion_title = webhook_data["discussion"]["title"] + repo_name = webhook_data["repo"]["name"] + + # Extract potential tags from the comment and discussion title + comment_tags = extract_tags_from_text(comment_content) + title_tags = extract_tags_from_text(discussion_title) + all_tags = list(set(comment_tags + title_tags)) + + print(f"🔍 All unique tags: {all_tags}") + + if not all_tags: + return ["No recognizable tags found in the discussion."] +``` + +This first part extracts and combines tags from both the comment content and discussion title. We use a set to deduplicate any tags that appear in both places. + + + +Processing both the comment and discussion title increases our chances of catching relevant tags. Users might mention tags in the title like "Missing pytorch tag" or in comments like "This needs #transformers". + + + +Next, we get our agent and process each tag: + +```python + # Get agent instance + agent = await get_agent() + if not agent: + return ["Error: Agent not configured (missing HF_TOKEN)"] + + # Process each tag + result_messages = [] + for tag in all_tags: + try: + # Use agent to process the tag + prompt = f""" + For the repository '{repo_name}', check if the tag '{tag}' already exists. + If it doesn't exist, add it via a pull request. + + Repository: {repo_name} + Tag to check/add: {tag} + """ + + print(f"🤖 Processing tag '{tag}' for repo '{repo_name}'") + response = await agent.run(prompt) + + # Parse agent response for success/failure + if "success" in response.lower(): + result_messages.append(f"✅ Tag '{tag}' processed successfully") + else: + result_messages.append(f"⚠️ Issue with tag '{tag}': {response}") + + except Exception as e: + error_msg = f"❌ Error processing tag '{tag}': {str(e)}" + print(error_msg) + result_messages.append(error_msg) + + return result_messages +``` + +The key insight here is that we give the agent a clear, structured prompt for each tag. The agent then: +1. Understands it needs to check the current tags first +2. Compares with the new tag we want to add +3. Creates a pull request if needed +4. Returns a summary of its actions + +This approach handles the complexity of tool orchestration automatically. + +### 2. Tag Extraction Logic + +Let's examine the tag extraction logic that feeds into our MCP processing: + +```python +import re +from typing import List + +# Recognized ML/AI tags for validation +RECOGNIZED_TAGS = { + "pytorch", "tensorflow", "jax", "transformers", "diffusers", + "text-generation", "text-classification", "question-answering", + "text-to-image", "image-classification", "object-detection", + "fill-mask", "token-classification", "translation", "summarization", + "feature-extraction", "sentence-similarity", "zero-shot-classification", + "image-to-text", "automatic-speech-recognition", "audio-classification", + "voice-activity-detection", "depth-estimation", "image-segmentation", + "video-classification", "reinforcement-learning", "tabular-classification", + "tabular-regression", "time-series-forecasting", "graph-ml", "robotics", + "computer-vision", "nlp", "cv", "multimodal", +} +``` + +This curated list of recognized tags helps us focus on relevant ML/AI tags and avoid adding inappropriate tags to repositories. + +Now the extraction function itself: + +```python +def extract_tags_from_text(text: str) -> List[str]: + """Extract potential tags from discussion text""" + text_lower = text.lower() + explicit_tags = [] + + # Pattern 1: "tag: something" or "tags: something" + tag_pattern = r"tags?:\s*([a-zA-Z0-9-_,\s]+)" + matches = re.findall(tag_pattern, text_lower) + for match in matches: + tags = [tag.strip() for tag in match.split(",")] + explicit_tags.extend(tags) + + # Pattern 2: "#hashtag" style + hashtag_pattern = r"#([a-zA-Z0-9-_]+)" + hashtag_matches = re.findall(hashtag_pattern, text_lower) + explicit_tags.extend(hashtag_matches) + + # Pattern 3: Look for recognized tags mentioned in natural text + mentioned_tags = [] + for tag in RECOGNIZED_TAGS: + if tag in text_lower: + mentioned_tags.append(tag) + + # Combine and deduplicate + all_tags = list(set(explicit_tags + mentioned_tags)) + + # Filter to only include recognized tags or explicitly mentioned ones + valid_tags = [] + for tag in all_tags: + if tag in RECOGNIZED_TAGS or tag in explicit_tags: + valid_tags.append(tag) + + return valid_tags +``` + +This function uses multiple strategies to extract tags: + +1. **Explicit patterns**: "tags: pytorch, transformers" or "tag: nlp" +2. **Hashtags**: "#pytorch #nlp" +3. **Natural mentions**: "This transformers model does text-generation" + +The validation step ensures we only suggest appropriate tags, preventing spam or irrelevant tags from being added. + + +## Performance Considerations + +When building production MCP clients, performance is critical for maintaining responsive webhook processing. Let's look at some of the considerations we've made. + +### 1. Agent Singleton Pattern + +The agent is created once and reused to avoid: +- Repeated MCP server startup overhead +- Tool loading delays +- Connection establishment costs + +This pattern is essential for webhook handlers that need to respond quickly. + +### 2. Async Processing + +All MCP operations are async to: +- Handle multiple webhook requests concurrently +- Avoid blocking the main FastAPI thread +- Provide responsive webhook responses + +The async nature allows your webhook handler to accept new requests while processing tags in the background. + +### 3. Background Task Processing + +FastAPI has a built in `BackgroundTasks` class that can be used to run tasks in the background. This is useful for running long running tasks without blocking the main thread. + +```python +from fastapi import BackgroundTasks + +@app.post("/webhook") +async def webhook_handler(request: Request, background_tasks: BackgroundTasks): + """Handle webhook and process in background""" + + # Validate webhook quickly + if request.headers.get("X-Webhook-Secret") != WEBHOOK_SECRET: + return {"error": "Invalid secret"} + + webhook_data = await request.json() + + # Process in background to return quickly + background_tasks.add_task(process_webhook_comment, webhook_data) + + return {"status": "accepted"} +``` + +This pattern ensures webhook responses are fast (under 1 second) while allowing complex tag processing to happen in the background. + + + +Webhook endpoints should respond within 10 seconds or the platform may consider them timed out. Using background tasks ensures you can always respond quickly while handling complex processing asynchronously. + + + +## Next Steps + +With our MCP client implemented, we can now: + +1. **Implement the Webhook Listener** - Create the FastAPI endpoint that receives Hub events +2. **Integrate Everything** - Connect webhooks, client, and server into a complete system +3. **Add Testing Interface** - Create a Gradio interface for development and monitoring +4. **Deploy and Test** - Validate the complete system in production + +In the next section, we'll implement the webhook listener that will trigger our MCP-powered tagging agent. + + + +The Agent class from `huggingface_hub` provides both MCP tool integration and language model reasoning, making it perfect for building intelligent automation workflows like our PR agent. + + \ No newline at end of file diff --git a/units/ja/unit3_1/quiz1.mdx b/units/ja/unit3_1/quiz1.mdx new file mode 100644 index 0000000..9e74b3b --- /dev/null +++ b/units/ja/unit3_1/quiz1.mdx @@ -0,0 +1,149 @@ +# Quiz 1: MCP Server Implementation + +Test your knowledge of MCP server concepts and implementation for the Pull Request Agent. + +### Q1: What is the primary role of an MCP Server in the Pull Request Agent architecture? + + + +### Q2: In the FastMCP implementation, why must all MCP tool functions return strings instead of Python objects? + + + +### Q3: When implementing the `add_new_tag` tool, what is the purpose of checking if a tag already exists before creating a pull request? + + + +### Q4: In the MCP server implementation, what happens when a model repository doesn't have an existing README.md file? + + + +### Q5: What is the significance of using `create_pr=True` in the `hf_api.create_commit()` function call? + + + +### Q6: Why does the MCP server implementation use extensive logging with emojis throughout the code? + + + +Congrats on finishing this Quiz 🥳! If you need to review any elements, take the time to revisit the chapter to reinforce your knowledge. \ No newline at end of file diff --git a/units/ja/unit3_1/quiz2.mdx b/units/ja/unit3_1/quiz2.mdx new file mode 100644 index 0000000..ee1a132 --- /dev/null +++ b/units/ja/unit3_1/quiz2.mdx @@ -0,0 +1,173 @@ +# Quiz 2: Pull Request Agent Integration + +Test your knowledge of the complete Pull Request Agent system including MCP client integration and webhook handling. + +### Q1: What is the primary purpose of the webhook listener in the Pull Request Agent architecture? + + + +### Q2: In the Agent-based MCP client implementation, how does the client connect to the MCP server? + + + +### Q3: Why does the webhook handler use FastAPI's `background_tasks.add_task()` instead of processing requests synchronously? + + + +### Q4: What is the purpose of validating the `X-Webhook-Secret` header in the webhook handler? + + + +### Q5: In the Agent implementation, what happens when `await agent_instance.load_tools()` is called? + + + +### Q6: How does the Agent intelligently use MCP tools when processing a natural language instruction? + + + +### Q7: What filtering logic determines whether a webhook event should trigger tag processing? + + + +Congrats on finishing this Quiz 🥳! If you need to review any elements, take the time to revisit the chapter to reinforce your knowledge. \ No newline at end of file diff --git a/units/ja/unit3_1/setting-up-the-project.mdx b/units/ja/unit3_1/setting-up-the-project.mdx new file mode 100644 index 0000000..4354672 --- /dev/null +++ b/units/ja/unit3_1/setting-up-the-project.mdx @@ -0,0 +1,133 @@ +# Setting up the Project + +In this section, we'll set up the development environment for our Pull Request Agent. + + + +We'll use modern Python tooling with `uv` for dependency management and create the necessary configuration files. If you're not familiar with `uv`, you can learn more about it [here](https://docs.astral.sh/uv/). + + + + +## Project Structure + +Let's start by creating the project directory and understanding the file structure: + +```bash +git clone https://huggingface.co/spaces/mcp-course/tag-this-repo +``` + +Our final project structure will look like this: + +``` +hf-pr-agent/ +├── mcp_server.py # Core MCP server with tagging tools +├── app.py # FastAPI webhook listener and agent +├── requirements.txt # Python dependencies +├── pyproject.toml # Project configuration +├── env.example # Environment variables template +├── cleanup.py # Development utility +``` + +## Dependencies and Configuration + +Let's walk through the dependencies and configuration for our project. + +### 1. Python Project Configuration + +We will use `uv` to create the `pyproject.toml` file to define our project: + + + +If you don't have `uv` installed, you can follow the instructions [here](https://docs.astral.sh/uv/getting-started/installation/). + + + +```toml +[project] +name = "mcp-course-unit3-example" +version = "0.1.0" +description = "FastAPI and Gradio app for Hugging Face Hub discussion webhooks" +readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "fastapi>=0.104.0", + "uvicorn[standard]>=0.24.0", + "gradio>=4.0.0", + "huggingface-hub[mcp]>=0.32.0", + "pydantic>=2.0.0", + "python-multipart>=0.0.6", + "requests>=2.31.0", + "python-dotenv>=1.0.0", + "fastmcp>=2.0.0", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src"] +``` + +For compatibility with various deployment platforms, the same is repeated in `requirements.txt` + +To create a virtual environment, run: + +```bash +uv venv +source .venv/bin/activate # or .venv/Scripts/activate on Windows +``` + +To install the dependencies, run: + +```bash +uv sync +``` + +### 2. Environment Configuration + +Create `env.example` to document required environment variables: + +```bash +# Hugging Face API Token (required) +# Get from: https://huggingface.co/settings/tokens +HF_TOKEN=hf_your_token_here + +# Webhook Secret (required for production) +# Use a strong, random string +WEBHOOK_SECRET=your-webhook-secret-here + +# Model for the agent (optional) +HF_MODEL=owner/model + +# Provider for MCP agent (optional) +HF_PROVIDER=huggingface +``` + +You will need to get your Hugging Face API token from [here](https://huggingface.co/settings/tokens). + +You will also need to generate a webhook secret. You can do this by running the following command: + +```bash +python -c "import secrets; print(secrets.token_hex(32))" +``` + +You will then need to add the webhook secret to your `.env` file based on the `env.example` file. + +## Next Steps + +With our project structure and environment set up, we're ready to: + +1. **Create the MCP Server** - Implement the core tagging functionality +2. **Build the Webhook Listener** - Handle incoming discussion events +3. **Integrate the Agent** - Connect MCP tools with webhook processing +4. **Test and Deploy** - Validate functionality and deploy to Spaces + +In the next section, we'll dive into creating our MCP server that will handle all the Hugging Face Hub interactions. + + + +Keep your `.env` file secure and never commit it to version control. The `.env` file should be added to your `.gitignore` file to prevent accidental exposure of secrets. + + \ No newline at end of file diff --git a/units/ja/unit3_1/webhook-listener.mdx b/units/ja/unit3_1/webhook-listener.mdx new file mode 100644 index 0000000..62e0fa3 --- /dev/null +++ b/units/ja/unit3_1/webhook-listener.mdx @@ -0,0 +1,519 @@ +# Webhook Listener + +The webhook listener is the entry point for our Pull Request Agent. It receives real-time events from the Hugging Face Hub when discussions are created or updated, triggering our MCP-powered tagging workflow. In this section, we'll implement a webhook handler using FastAPI. + +## Understanding Webhook Integration + +Following the [Hugging Face Webhooks Guide](https://raw.githubusercontent.com/huggingface/hub-docs/refs/heads/main/docs/hub/webhooks-guide-discussion-bot.md), our webhook listener validates incoming requests and processes discussion events in real-time. + +![Webhook Creation](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/webhook-creation.png) + +### Webhook Event Flow + +Understanding the webhook flow is crucial for building a reliable listener: + +1. **User Action**: Someone creates a comment in a model repository discussion +2. **Hub Event**: Hugging Face generates a webhook event +3. **Webhook Delivery**: Hub sends POST request to our endpoint +4. **Authentication**: We validate the webhook secret +5. **Processing**: Extract tags from the comment content +6. **Action**: Use MCP tools to create pull requests for new tags + + + +Webhooks are push notifications - the Hugging Face Hub actively sends events to your application rather than you polling for changes. This enables real-time responses to discussions and comments. + + + +## FastAPI Webhook Application + +Let's build our webhook listener step by step, starting with the foundation and building up to the complete processing logic. + +### 1. Application Setup + +First, let's set up the basic FastAPI application with all necessary imports and configuration: + +```python +import os +import json +from datetime import datetime +from typing import List, Dict, Any, Optional + +from fastapi import FastAPI, Request, BackgroundTasks +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +``` + +These imports give us everything we need to build a robust webhook handler. `FastAPI` provides the web framework, `BackgroundTasks` enables async processing, and the typing imports help with data validation. + +Now let's configure our application: + +```python +# Configuration +WEBHOOK_SECRET = os.getenv("WEBHOOK_SECRET") +HF_TOKEN = os.getenv("HF_TOKEN") + +# Simple storage for processed operations +tag_operations_store: List[Dict[str, Any]] = [] + +app = FastAPI(title="HF Tagging Bot") +app.add_middleware(CORSMiddleware, allow_origins=["*"]) +``` + +This configuration sets up: +- **Webhook secret**: For validating incoming webhooks +- **HF token**: For authenticating with the Hub API +- **Operations store**: In-memory storage for monitoring processed operations +- **CORS middleware**: Allows cross-origin requests for the web interface + + +The `tag_operations_store` list keeps track of recent webhook processing operations. This is useful for debugging and monitoring, but in production you might want to use a database or limit the size of this list. + + +### 2. Webhook Data Models + +Based on the [Hugging Face webhook documentation](https://raw.githubusercontent.com/huggingface/hub-docs/refs/heads/main/docs/hub/webhooks-guide-discussion-bot.md), we need to understand the webhook data structure: + +```python +class WebhookEvent(BaseModel): + event: Dict[str, str] # Contains action and scope information + comment: Dict[str, Any] # Comment content and metadata + discussion: Dict[str, Any] # Discussion information + repo: Dict[str, str] # Repository details +``` + +This Pydantic model helps us understand the webhook structure. + +The key fields we care about are: +- `event.action`: Usually "create" for new comments +- `event.scope`: Usually "discussion.comment" for comment events +- `comment.content`: The actual comment text +- `repo.name`: The repository where the comment was made + +### 3. Core Webhook Handler + +Now for the main webhook handler - this is where the important part happens. Let's break it down into digestible pieces: + +```python +@app.post("/webhook") +async def webhook_handler(request: Request, background_tasks: BackgroundTasks): + """ + Handle incoming webhooks from Hugging Face Hub + Following the pattern from: https://raw.githubusercontent.com/huggingface/hub-docs/refs/heads/main/docs/hub/webhooks-guide-discussion-bot.md + """ + print("🔔 Webhook received!") + + # Step 1: Validate webhook secret (security) + webhook_secret = request.headers.get("X-Webhook-Secret") + if webhook_secret != WEBHOOK_SECRET: + print("❌ Invalid webhook secret") + return {"error": "incorrect secret"}, 400 +``` + +The first step is security validation. We check the `X-Webhook-Secret` header against our configured secret to ensure the webhook is legitimate. + + + +Always validate webhook secrets! Without this check, anyone could send fake webhook requests to your application. The secret acts as a shared password between Hugging Face and your application. + + + +Next, let's parse and validate the webhook data: + +```python + # Step 2: Parse webhook data + try: + webhook_data = await request.json() + print(f"📥 Webhook data: {json.dumps(webhook_data, indent=2)}") + except Exception as e: + print(f"❌ Error parsing webhook data: {str(e)}") + return {"error": "invalid JSON"}, 400 + + # Step 3: Validate event structure + event = webhook_data.get("event", {}) + if not event: + print("❌ No event data in webhook") + return {"error": "missing event data"}, 400 +``` + +This parsing step handles potential JSON errors gracefully and validates that we have the expected event structure. + +Now for the event filtering logic: + +```python + # Step 4: Check if this is a discussion comment creation + # Following the webhook guide pattern: + if ( + event.get("action") == "create" and + event.get("scope") == "discussion.comment" + ): + print("✅ Valid discussion comment creation event") + + # Process in background to return quickly to Hub + background_tasks.add_task(process_webhook_comment, webhook_data) + + return { + "status": "accepted", + "message": "Comment processing started", + "timestamp": datetime.now().isoformat() + } + else: + print(f"ℹ️ Ignoring event: action={event.get('action')}, scope={event.get('scope')}") + return { + "status": "ignored", + "reason": "Not a discussion comment creation" + } +``` + +This filtering ensures we only process the events we care about - new discussion comments. We ignore other events like repository creation, model uploads, etc. + +We use FastAPI's `background_tasks.add_task()` to process the webhook asynchronously. This allows us to return a response quickly (within seconds) while the actual tag processing happens in the background. + + + +Webhook endpoints should respond within 10 seconds, or the sending platform may consider them failed. Using background tasks ensures fast responses while allowing complex processing to happen asynchronously. + + + +### 4. Comment Processing Logic + +Now let's implement the core comment processing function that does the actual tag extraction and MCP tool usage: + +```python +async def process_webhook_comment(webhook_data: Dict[str, Any]): + """ + Process webhook comment to detect and add tags + Integrates with our MCP client for Hub interactions + """ + print("🏷️ Starting process_webhook_comment...") + + try: + # Extract comment and repository information + comment_content = webhook_data["comment"]["content"] + discussion_title = webhook_data["discussion"]["title"] + repo_name = webhook_data["repo"]["name"] + discussion_num = webhook_data["discussion"]["num"] + comment_author = webhook_data["comment"]["author"].get("id", "unknown") + + print(f"📝 Comment from {comment_author}: {comment_content}") + print(f"📰 Discussion: {discussion_title}") + print(f"📦 Repository: {repo_name}") +``` + +This initial section extracts all the relevant information from the webhook data. We get both the comment content and discussion title since tags might be mentioned in either place. + +Next, we extract and process the tags: + +```python + # Extract potential tags from comment and title + comment_tags = extract_tags_from_text(comment_content) + title_tags = extract_tags_from_text(discussion_title) + all_tags = list(set(comment_tags + title_tags)) + + print(f"🔍 Found tags: {all_tags}") + + # Store operation for monitoring + operation = { + "timestamp": datetime.now().isoformat(), + "repo_name": repo_name, + "discussion_num": discussion_num, + "comment_author": comment_author, + "extracted_tags": all_tags, + "comment_preview": comment_content[:100] + "..." if len(comment_content) > 100 else comment_content, + "status": "processing" + } + tag_operations_store.append(operation) +``` + +We combine tags from both sources and create an operation record for monitoring. This record tracks the progress of each webhook processing operation. + + + +Storing operation records is crucial for debugging and monitoring. When something goes wrong, you can look at recent operations to understand what happened and why. + + + +Now for the MCP agent integration: + +```python + if not all_tags: + operation["status"] = "no_tags" + operation["message"] = "No recognizable tags found" + print("❌ No tags found to process") + return + + # Get MCP agent for tag processing + agent = await get_agent() + if not agent: + operation["status"] = "error" + operation["message"] = "Agent not configured (missing HF_TOKEN)" + print("❌ No agent available") + return + + # Process each extracted tag + operation["results"] = [] + for tag in all_tags: + try: + print(f"🤖 Processing tag '{tag}' for repo '{repo_name}'") + + # Create prompt for agent to handle tag processing + prompt = f""" + Analyze the repository '{repo_name}' and determine if the tag '{tag}' should be added. + + First, check the current tags using get_current_tags. + If '{tag}' is not already present and it's a valid tag, add it using add_new_tag. + + Repository: {repo_name} + Tag to process: {tag} + + Provide a clear summary of what was done. + """ + + response = await agent.run(prompt) + print(f"🤖 Agent response for '{tag}': {response}") + + # Parse response and store result + tag_result = { + "tag": tag, + "response": response, + "timestamp": datetime.now().isoformat() + } + operation["results"].append(tag_result) + + except Exception as e: + error_msg = f"❌ Error processing tag '{tag}': {str(e)}" + print(error_msg) + operation["results"].append({ + "tag": tag, + "error": str(e), + "timestamp": datetime.now().isoformat() + }) + + operation["status"] = "completed" + print(f"✅ Completed processing {len(all_tags)} tags") +``` + +This section handles the core business logic: +1. **Validation**: Ensure we have tags to process and an available agent +2. **Processing**: For each tag, create a natural language prompt for the agent +3. **Recording**: Store all results for monitoring and debugging +4. **Error handling**: Gracefully handle errors for individual tags + +The agent prompt is carefully crafted to instruct the AI on exactly what steps to take: check current tags first, then add the new tag if appropriate. + + +### 5. Health and Monitoring Endpoints + +Besides the webhook handler, we need endpoints for monitoring and debugging. Let's add these essential endpoints: + +```python +@app.get("/") +async def root(): + """Root endpoint with basic information""" + return { + "name": "HF Tagging Bot", + "status": "running", + "description": "Webhook listener for automatic model tagging", + "endpoints": { + "webhook": "/webhook", + "health": "/health", + "operations": "/operations" + } + } +``` + +The root endpoint provides basic information about your service and its available endpoints. + +```python +@app.get("/health") +async def health_check(): + """Health check endpoint for monitoring""" + agent = await get_agent() + + return { + "status": "healthy", + "timestamp": datetime.now().isoformat(), + "components": { + "webhook_secret": "configured" if WEBHOOK_SECRET else "missing", + "hf_token": "configured" if HF_TOKEN else "missing", + "mcp_agent": "ready" if agent else "not_ready" + } + } +``` + +The health check endpoint validates that all your components are properly configured. This is essential for production monitoring. + +```python +@app.get("/operations") +async def get_operations(): + """Get recent tag operations for monitoring""" + # Return last 50 operations + recent_ops = tag_operations_store[-50:] if tag_operations_store else [] + return { + "total_operations": len(tag_operations_store), + "recent_operations": recent_ops + } +``` + +The operations endpoint lets you see recent webhook processing activity, which is invaluable for debugging and monitoring. + + + +Health and monitoring endpoints are crucial for production deployments. They help you quickly identify configuration issues and monitor your application's activity without digging through logs. + + + +## Webhook Configuration on Hugging Face Hub + +Now that we have our webhook listener ready, let's configure it on the Hugging Face Hub. This is where we connect our application to real repository events. + +### 1. Create Webhook in Settings + +Following the [webhook setup guide](https://huggingface.co/docs/hub/webhooks-guide-discussion-bot): + +![Webhook Settings](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/webhook-creation.png) + +Navigate to your [Hugging Face Settings](https://huggingface.co/settings/webhooks) and configure: + +1. **Target Repositories**: Specify which repositories to monitor +2. **Webhook URL**: Your deployed application endpoint (e.g., `https://your-space.hf.space/webhook`) +3. **Secret**: Use the same secret from your `WEBHOOK_SECRET` environment variable +4. **Events**: Subscribe to "Community (PR & discussions)" events + + + +Start with one or two test repositories before configuring webhooks for many repositories. This lets you validate your application works correctly before scaling up. + + + +### 2. Space URL Configuration + +For Hugging Face Spaces deployment, you'll need to get your direct URL: + +![Direct URL](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/direct-url.png) + +The process is: +1. Click "Embed this Space" in your Space settings +2. Copy the "Direct URL" +3. Append `/webhook` to create your webhook endpoint +4. Update your webhook configuration with this URL + +For example, if your Space URL is `https://username-space-name.hf.space`, your webhook endpoint would be `https://username-space-name.hf.space/webhook`. + +![Space URL](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/direct-url.png) + +## Testing the Webhook Listener + +Testing is crucial before deploying to production. Let's walk through different testing approaches: + +### 1. Local Testing + +You can test your webhook handler locally using a simple script: + +```python +# test_webhook_local.py +import requests +import json + +# Test data matching webhook format +test_webhook_data = { + "event": { + "action": "create", + "scope": "discussion.comment" + }, + "comment": { + "content": "This model needs tags: pytorch, transformers", + "author": {"id": "test-user"} + }, + "discussion": { + "title": "Missing tags", + "num": 1 + }, + "repo": { + "name": "test-user/test-model" + } +} + +# Send test webhook +response = requests.post( + "http://localhost:8000/webhook", + json=test_webhook_data, + headers={"X-Webhook-Secret": "your-test-secret"} +) + +print(f"Status: {response.status_code}") +print(f"Response: {response.json()}") +``` + +This script simulates a real webhook request, allowing you to test your handler without waiting for real events. + +### 2. Simulation Endpoint for Development + +You can also add a simulation endpoint to your FastAPI application for easier testing: + +```python +@app.post("/simulate_webhook") +async def simulate_webhook( + repo_name: str, + discussion_title: str, + comment_content: str +) -> str: + """Simulate webhook for testing purposes""" + + # Create mock webhook data + mock_webhook_data = { + "event": { + "action": "create", + "scope": "discussion.comment" + }, + "comment": { + "content": comment_content, + "author": {"id": "test-user"} + }, + "discussion": { + "title": discussion_title, + "num": 999 + }, + "repo": { + "name": repo_name + } + } + + # Process the simulated webhook + await process_webhook_comment(mock_webhook_data) + + return f"Simulated webhook processed for {repo_name}" +``` + +This endpoint makes it easy to test different scenarios through your application's interface. + + +Simulation endpoints are incredibly useful during development. They let you test different tag combinations and edge cases without creating actual repository discussions. + + +## Expected Webhook Result + +When everything is working correctly, you should see results like the discussion bot example: + +![Discussion Result](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/webhooks-guides/001-discussion-bot/discussion-result.png) + + + +This screenshot shows a successful webhook processing where the bot creates a pull request in response to a discussion comment. + +## Next Steps + +With our webhook listener implemented, we now have: + +1. **Secure webhook validation** following Hugging Face best practices +2. **Real-time event processing** with background task handling +3. **MCP integration** for intelligent tag management +4. **Monitoring and debugging** capabilities + +In the next section, we'll integrate everything into a complete Pull Request Agent that demonstrates the full workflow from webhook to PR creation. + + + +Always return webhook responses quickly (within 10 seconds) to avoid timeouts. Use background tasks for longer processing operations like MCP tool execution and pull request creation. + + \ No newline at end of file From 0e493ddaaecdece212f8366792d98dd9f2fa092e Mon Sep 17 00:00:00 2001 From: masato Date: Fri, 27 Jun 2025 08:38:27 +0900 Subject: [PATCH 2/3] =?UTF-8?q?=E3=83=A6=E3=83=8B=E3=83=83=E3=83=881?= =?UTF-8?q?=E3=81=8B=E3=82=89=E3=83=A6=E3=83=8B=E3=83=83=E3=83=883?= =?UTF-8?q?=E3=81=BE=E3=81=A7=E3=81=AE=E6=97=A5=E6=9C=AC=E8=AA=9E=E3=83=89?= =?UTF-8?q?=E3=82=AD=E3=83=A5=E3=83=A1=E3=83=B3=E3=83=88=E3=82=92=E6=9B=B4?= =?UTF-8?q?=E6=96=B0=E3=81=97=E3=80=81MCP=E3=82=B3=E3=83=BC=E3=82=B9?= =?UTF-8?q?=E3=81=AE=E5=86=85=E5=AE=B9=E3=82=92=E5=BC=B7=E5=8C=96=E3=81=97?= =?UTF-8?q?=E3=81=BE=E3=81=97=E3=81=9F=E3=80=82=E5=85=B7=E4=BD=93=E7=9A=84?= =?UTF-8?q?=E3=81=AB=E3=81=AF=E3=80=81=E8=AA=8D=E5=AE=9A=E8=A8=BC=E5=8F=96?= =?UTF-8?q?=E5=BE=97=E3=81=AE=E3=82=BB=E3=82=AF=E3=82=B7=E3=83=A7=E3=83=B3?= =?UTF-8?q?=E3=80=81Gradio=E7=B5=B1=E5=90=88=E3=80=81MCP=E3=82=AF=E3=83=A9?= =?UTF-8?q?=E3=82=A4=E3=82=A2=E3=83=B3=E3=83=88=E3=81=AE=E5=BD=B9=E5=89=B2?= =?UTF-8?q?=E3=80=81=E3=82=AF=E3=82=A4=E3=82=BA=E3=81=AE=E5=86=85=E5=AE=B9?= =?UTF-8?q?=E3=82=92=E6=97=A5=E6=9C=AC=E8=AA=9E=E3=81=AB=E7=BF=BB=E8=A8=B3?= =?UTF-8?q?=E3=81=97=E3=80=81=E5=90=84=E3=82=BB=E3=82=AF=E3=82=B7=E3=83=A7?= =?UTF-8?q?=E3=83=B3=E3=81=AE=E7=90=86=E8=A7=A3=E3=82=92=E6=B7=B1=E3=82=81?= =?UTF-8?q?=E3=82=8B=E3=81=9F=E3=82=81=E3=81=AE=E6=83=85=E5=A0=B1=E3=82=92?= =?UTF-8?q?=E8=BF=BD=E5=8A=A0=E3=81=97=E3=81=BE=E3=81=97=E3=81=9F=E3=80=82?= =?UTF-8?q?=E3=81=BE=E3=81=9F=E3=80=81Slack=E9=80=9A=E7=9F=A5=E6=A9=9F?= =?UTF-8?q?=E8=83=BD=E3=82=92=E5=90=AB=E3=82=80=E8=87=AA=E5=8B=95=E5=8C=96?= =?UTF-8?q?=E3=82=B7=E3=82=B9=E3=83=86=E3=83=A0=E3=81=AE=E6=A7=8B=E7=AF=89?= =?UTF-8?q?=E3=81=AB=E9=96=A2=E3=81=99=E3=82=8B=E5=86=85=E5=AE=B9=E3=82=82?= =?UTF-8?q?=E6=95=B4=E5=82=99=E3=81=97=E3=81=BE=E3=81=97=E3=81=9F=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- units/ja/unit1/certificate.mdx | 8 +- units/ja/unit1/gradio-mcp.mdx | 114 ++--- units/ja/unit1/mcp-clients.mdx | 176 +++---- units/ja/unit1/quiz1.mdx | 96 ++-- units/ja/unit1/quiz2.mdx | 80 ++-- units/ja/unit1/unit1-recap.mdx | 58 +-- units/ja/unit3/certificate.mdx | 11 +- units/ja/unit3/conclusion.mdx | 202 ++++----- units/ja/unit3/github-actions-integration.mdx | 308 ++++++------- units/ja/unit3/slack-notification.mdx | 428 +++++++++--------- 10 files changed, 740 insertions(+), 741 deletions(-) diff --git a/units/ja/unit1/certificate.mdx b/units/ja/unit1/certificate.mdx index cada2a3..f116595 100644 --- a/units/ja/unit1/certificate.mdx +++ b/units/ja/unit1/certificate.mdx @@ -1,8 +1,8 @@ -# Get your certificate! +# 認定証を取得しましょう！ -Well done! You've completed the first unit of the MCP course. Now it's time to take the exam to get your certificate. +よくできました！MCPコースの最初のユニットを完了しました。今度は認定証を取得するために試験を受ける時です。 -Below is a quiz to check your understanding of the unit. +以下はユニットの理解を確認するためのクイズです。

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