core (ai/mcp): update experimental MCP client documentation for Streamable HTTP transport usage

content/cookbook/01-next/73-mcp-tools.mdx

@@ -12,10 +12,15 @@ The AI SDK supports Model Context Protocol (MCP) tools by offering a lightweight
 
 Let's create a route handler for `/api/completion` that will generate text based on the input prompt and MCP tools that can be called at any time during a generation. The route will call the `streamText` function from the `ai` module, which will then generate text based on the input prompt and stream it to the client.
 
+To use the `StreamableHTTPClientTransport`, you will need to install the official Typescript SDK for Model Context Protocol:
+
+<Snippet text="pnpm install @modelcontextprotocol/sdk" />
+
 ```ts filename="app/api/completion/route.ts"
 import { experimental_createMCPClient, streamText } from 'ai';
 import { Experimental_StdioMCPTransport } from 'ai/mcp-stdio';
 import { openai } from '@ai-sdk/openai';
+import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';
 
 export async function POST(req: Request) {
   const { prompt }: { prompt: string } = await req.json();
@@ -38,17 +43,17 @@ export async function POST(req: Request) {
       },
     });
 
-    // Similarly to the stdio example, you can pass in your own custom transport as long as it implements the `MCPTransport` interface:
-    const transport = new MyCustomTransport({
-      // ...
-    });
-    const customTransportClient = await experimental_createMCPClient({
+    // Similarly to the stdio example, you can pass in your own custom transport as long as it implements the `MCPTransport` interface (e.g. `StreamableHTTPClientTransport`):
+    const transport = new StreamableHTTPClientTransport(
+      new URL('http://localhost:3000/mcp'),
+    );
+    const customClient = await experimental_createMCPClient({
       transport,
     });
 
     const toolSetOne = await stdioClient.tools();
     const toolSetTwo = await sseClient.tools();
-    const toolSetThree = await customTransportClient.tools();
+    const toolSetThree = await customClient.tools();
     const tools = {
       ...toolSetOne,
       ...toolSetTwo,
@@ -63,7 +68,15 @@ export async function POST(req: Request) {
       onFinish: async () => {
         await stdioClient.close();
         await sseClient.close();
-        await customTransportClient.close();
+        await customClient.close();
+      },
+      // Closing clients onError is optional
+      // - Closing: Immediately frees resources, prevents hanging connections
+      // - Not closing: Keeps connection open for retries
+      onError: async error => {
+        await stdioClient.close();
+        await sseClient.close();
+        await customClient.close();
       },
     });
 

content/docs/03-ai-sdk-core/15-tools-and-tool-calling.mdx

@@ -679,7 +679,7 @@ Create an MCP client using either:
 
 - `SSE` (Server-Sent Events): Uses HTTP-based real-time communication, better suited for remote servers that need to send data over the network
 - `stdio`: Uses standard input and output streams for communication, ideal for local tool servers running on the same machine (like CLI tools or local services)
-- Custom transport: Bring your own transport by implementing the `MCPTransport` interface
+- Custom transport: Bring your own transport by implementing the `MCPTransport` interface, ideal when implementing transports from MCP's official Typescript SDK (e.g. `StreamableHTTPClientTransport`)
 
 #### SSE Transport
 
@@ -719,18 +719,30 @@ const mcpClient = await createMCPClient({
 
 #### Custom Transport
 
-You can also bring your own transport by implementing the `MCPTransport` interface:
+You can also bring your own transport, as long as it implements the `MCPTransport` interface. Below is an example of using the new `StreamableHTTPClientTransport` from MCP's official Typescript SDK:
 
 ```typescript
-import { MCPTransport, createMCPClient } from 'ai';
+import {
+  MCPTransport,
+  experimental_createMCPClient as createMCPClient,
+} from 'ai';
+import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';
 
+const url = new URL('http://localhost:3000/mcp');
 const mcpClient = await createMCPClient({
-  transport: new MyCustomTransport({
-    // ...
+  transport: new StreamableHTTPClientTransport(url, {
+    sessionId: 'session_123',
   }),
 });
 ```
 
+<Note>
+  The client returned by the `experimental_createMCPClient` function is a
+  lightweight client intended for use in tool conversion. It currently does not
+  support all features of the full MCP client, such as: authorization, session
+  management, resumable streams, and receiving notifications.
+</Note>
+
 #### Closing the MCP Client
 
 After initialization, you should close the MCP client based on your usage pattern:

examples/mcp/README.md

@@ -18,6 +18,20 @@ pnpm install
 pnpm build
 ```
 
+## Streamable HTTP Transport (Stateful)
+
+Start server
+
+```sh
+pnpm http:server
+```
+
+Run example:
+
+```sh
+pnpm http:client
+```
+
 ## Stdio Transport
 
 Build
@@ -32,7 +46,7 @@ Run example:
 pnpm stdio:client
 ```
 
-## SSE Transport
+## SSE Transport (Legacy)
 
 Start server
 

examples/mcp/package.json

@@ -7,13 +7,15 @@
     "sse:client": "tsx src/sse/client.ts",
     "stdio:build": "tsc src/stdio/server.ts --outDir src/stdio/dist --target es2023 --module nodenext",
     "stdio:client": "tsx src/stdio/client.ts",
+    "http:server": "tsx src/http/server.ts",
+    "http:client": "tsx src/http/client.ts",
     "custom-transport:build": "tsc src/custom-transport/server.ts --outDir src/custom-transport/dist --target es2023 --module nodenext",
     "custom-transport:client": "tsx src/custom-transport/client.ts",
     "type-check": "tsc --noEmit"
   },
   "dependencies": {
+    "@modelcontextprotocol/sdk": "1.10.2",
     "@ai-sdk/openai": "1.3.20",
-    "@modelcontextprotocol/sdk": "^1.7.0",
     "ai": "4.3.11",
     "dotenv": "16.4.5",
     "express": "5.0.1",
@@ -25,4 +27,4 @@
     "tsx": "4.19.2",
     "typescript": "5.6.3"
   }
-}
+}

examples/mcp/src/http/client.ts

@@ -0,0 +1,37 @@
+import { openai } from '@ai-sdk/openai';
+import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';
+import { experimental_createMCPClient, generateText } from 'ai';
+import 'dotenv/config';
+
+async function main() {
+  const transport = new StreamableHTTPClientTransport(
+    new URL('http://localhost:3000/mcp'),
+  );
+
+  const mcpClient = await experimental_createMCPClient({
+    transport,
+  });
+
+  try {
+    const tools = await mcpClient.tools();
+
+    const { text: answer } = await generateText({
+      model: openai('gpt-4o-mini'),
+      tools,
+      maxSteps: 10,
+      onStepFinish: async ({ toolResults }) => {
+        console.log(`STEP RESULTS: ${JSON.stringify(toolResults, null, 2)}`);
+      },
+      system: 'You are a helpful chatbot',
+      prompt: 'Look up information about user with the ID foo_123',
+    });
+
+    console.log(`FINAL ANSWER: ${answer}`);
+  } catch (error) {
+    console.error('Error:', error);
+  } finally {
+    await mcpClient.close();
+  }
+}
+
+main();

examples/mcp/src/http/server.ts

@@ -0,0 +1,105 @@
+import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
+import { StreamableHTTPServerTransport } from '@modelcontextprotocol/sdk/server/streamableHttp.js';
+import express from 'express';
+import { z } from 'zod';
+
+// Stateless Mode: see https://github.com/modelcontextprotocol/typescript-sdk/tree/main/src/examples#stateless-mode for more details
+
+const app = express();
+app.use(express.json());
+
+app.post('/mcp', async (req, res) => {
+  const server = new McpServer({
+    name: 'example-http-server',
+    version: '1.0.0',
+  });
+
+  server.tool(
+    'get-user-info',
+    'Get user info',
+    {
+      userId: z.string(),
+    },
+    async ({ userId }) => {
+      return {
+        content: [
+          {
+            type: 'text',
+            text: `Here is information about user ${userId}:`,
+          },
+          {
+            type: 'text',
+            text: `Name: John Doe`,
+          },
+          {
+            type: 'text',
+            text: `Email: [email protected]`,
+          },
+          {
+            type: 'text',
+            text: `Age: 30`,
+          },
+        ],
+      };
+    },
+  );
+
+  try {
+    const transport = new StreamableHTTPServerTransport({
+      sessionIdGenerator: undefined,
+    });
+    await server.connect(transport);
+    await transport.handleRequest(req, res, req.body);
+    res.on('close', () => {
+      transport.close();
+      server.close();
+    });
+  } catch (error) {
+    console.error('Error handling MCP request:', error);
+    if (!res.headersSent) {
+      res.status(500).json({
+        jsonrpc: '2.0',
+        error: {
+          code: -32603,
+          message: 'Internal server error',
+        },
+        id: null,
+      });
+    }
+  }
+});
+
+app.get('/mcp', async (_req, res) => {
+  console.log('Received GET MCP request');
+  res.writeHead(405).end(
+    JSON.stringify({
+      jsonrpc: '2.0',
+      error: {
+        code: -32000,
+        message: 'Method not allowed.',
+      },
+      id: null,
+    }),
+  );
+});
+
+app.delete('/mcp', async (_req, res) => {
+  console.log('Received DELETE MCP request');
+  res.writeHead(405).end(
+    JSON.stringify({
+      jsonrpc: '2.0',
+      error: {
+        code: -32000,
+        message: 'Method not allowed.',
+      },
+      id: null,
+    }),
+  );
+});
+
+app.listen(3000);
+
+process.on('SIGINT', async () => {
+  console.log('Shutting down server...');
+  process.exit(0);
+});

examples/next-openai/app/mcp/chat/route.ts

@@ -0,0 +1,42 @@
+import { openai } from '@ai-sdk/openai';
+import { StreamableHTTPClientTransport } from '@modelcontextprotocol/sdk/client/streamableHttp';
+import { experimental_createMCPClient, streamText } from 'ai';
+
+export async function POST(req: Request) {
+  const url = new URL('http://localhost:3000/mcp/server');
+  const transport = new StreamableHTTPClientTransport(url);
+
+  const [client, { messages }] = await Promise.all([
+    experimental_createMCPClient({
+      transport,
+    }),
+    req.json(),
+  ]);
+
+  try {
+    const tools = await client.tools();
+
+    const result = streamText({
+      model: openai('gpt-4o-mini'),
+      tools,
+      maxSteps: 5,
+      onStepFinish: async ({ toolResults }) => {
+        console.log(`STEP RESULTS: ${JSON.stringify(toolResults, null, 2)}`);
+      },
+      system: 'You are a helpful chatbot capable of basic arithmetic problems',
+      messages,
+      onFinish: async () => {
+        await client.close();
+      },
+      // Optional, enables immediate clean up of resources but connection will not be retained for retries:
+      // onError: async error => {
+      //   await client.close();
+      // },
+    });
+
+    return result.toDataStreamResponse();
+  } catch (error) {
+    console.error(error);
+    return Response.json({ error: 'Unexpected error' }, { status: 500 });
+  }
+}