Teradata MCP Server

Overview

The Teradata MCP server provides a set of tools and prompts for interacting with Teradata databases, enabling AI agents and users to query, analyze, and manage their data efficiently.

This is an open project and we welcome contributions via pull requests.

TLDR; I want to try it locally now

If you have Docker and a client that can connect MCP servers via SSE, copy the code below, update the connection string set in DATABASE_URI with your database connection details and run it:

export DATABASE_URI=teradata://username:password@host:1025
git clone https://github.com/Teradata/teradata-mcp-server.git
cd teradata-mcp-server
docker compose up

You can now use it with clients supporting SSE such as Visual Studio Code.

Key features

Available tools and prompts

We are providing groupings of tools and associated helpful prompts

Base tools, prompts and resources to interact with your Teradata platform:
- Base Tools
DBA tools, prompts and resources to facilitate your platform administration tasks:
- DBA Tools
Data Quality tools, prompts and resources accelerate exploratory data analysis:
- Data Quality Tools
Security tools, prompts and resources to resolve permissions:
- Security Tools
Feature Store tools, prompts and resources to manage the Enterprise Feature Store:
- Feature Store Tools
RAG tools, prompts and resources to manage vector store creation and use
- RAG Tools
Custom Tools to easily implement tools for custom actions based on your data and business context

Adding custom tools

You may add define custom "query" tools in the custom_tools.yaml file or in any file ending with _tools.yaml. Simply specify the tool name, description and SQL query to be executed. No parameters are supported at this point.

The Test directory contains a simple ClientChatBot tool for testing tools.

Environment Set Up

If you do not have a Teradata system, get a sandbox for free and right away at ClearScape Analytics Experience!

The two recommended ways to run this server are using uv and Docker.

Jump to next section for the docker option.

Using uv

Make sure you have uv installed on your system, installation instructions can be found at https://github.com/astral-sh/uv .

Step 1 - Clone the mcp-server repository with

On Windows

git clone https://github.com/Teradata/teradata-mcp-server.git
cd teradata-mcp-server
uv sync
.venv/Scripts/activate

On Mac/Linux

git clone https://github.com/Teradata/teradata-mcp-server.git
cd teradata-mcp-server
uv sync
source .venv/bin/activate

Step 2 - Configure the server

The server will connect to your Teradata instance and to the clients over stdio (default) or server-sent events (SSE) or streamable-http. To configure the connections set the following environment variables in your shell or in a .env file in the current directory (by updating and renaming the provided env file).

Database connection string with the following format: teradata://username:password@host:1025/[schemaname]

Replace username, password, host with your systems and credential details, set default schema with schemaname

Optionally, for transport connectivity, SSE transport is in the process of being decommissioned and replaces with streamable-http, we will continue to support SSE for the moment. Set MCP_TRANSPORT to sse and your host IP and port number with MCP_HOST (defaults to 127.0.0.1) and MCP_PORT (defaults to 8001):

Optionally, for transport connectivity, set MCP_TRANSPORT to streamable-http and your host IP and port number with MCP_HOST (defaults to 127.0.0.1) and MCP_PORT (defaults to 8001) and MCP_PATH (defaults to /mcp/):

Configuration example:

export DATABASE_URI=teradata://username:password@host:1025/schemaname

# Enables transport communication as stdio, sse, streamable-http
export MCP_TRANSPORT=stdio 
export MCP_HOST=127.0.0.1
export MCP_PORT=8001
export MCP_PATH=/mcp/

Step 3 - Run the server with uv

uv run teradata-mcp-server

Using Docker

Clone this repository

git clone https://github.com/Teradata/teradata-mcp-server.git
cd teradata-mcp-server

The server expects the Teradata URI string via the DATABASE_URI environment variable. You may update the docker-compose.yaml file or setup the environment variable with your system's connection details:

export DATABASE_URI=teradata://username:password@host:1025/databaseschema

Run the MCP server with SSE (default)

This starts only the core Teradata MCP server (with stdio or SSE communication):

docker compose up

The server will be available on port 8001 (or the value of the PORT environment variable).

Run the MCP server with REST

Alternatively, you can expose your tools, prompts and resources as REST endpoints using the rest profile.

You can set an API key using the environment variable MCPO_API_KEY. Caution: there is no default value, not no authorization needed by default.

The default port is 8002.

export MCPO_API_KEY=top-secret
docker compose --profile rest up

Using with Visual Studio Code Co-pilot

Visual Studio Code Co-pilot provides a simple and interactive way to test this server. Follow the instructions below to run and configure the server, set co-pilot to Agent mode, and use it.

Detailed instructions on configuring MCP server with Visual Studio Code can be found in Visual Studio Code documentation.

Using Server-Sent Events (SSE) (recommended)

You can use uv or Docker to start the server.

Using uv, ensure that SSE is enabled (not by default) and the host port are defined. You can do this with setting the environment variables below or in the .env file):

export MCP_TRANSPORT=sse
export MCP_HOST=127.0.0.1
export MCP_PORT=8001

uv run teradata-mcp-server

Alternatively, start with Docker (defaults to SSE):

docker compose up

Add the server in VS Code:

Open the Command Palette (View>Command Palette)
select "MCP: Add Server"
select "HTTP Server Sent Events"
enter URL for the location of the server e.g. http://127.0.0.1:8001/sse
enter name of the server for the id
select user space
the settings.json file should open
add the args so that it looks like:

   "mcp": {
        "servers": {
            "teradataSSE": {
                "type": "sse",
                "url": "http://127.0.0.1:8001/sse"
            }
        }
    }

within the settings.json file or you can "MCP: Start Server"

Using stdio

To run the server with stdio set MCP_TRANSPORT=stdio in your .env file or via the MCP_TRANSPORT environment variable.

export MCP_TRANSPORT=stdio
uv run teradata-mcp-server

Add the server in VS Code:

Open the Command Palette (View>Command Palette)
select "MCP: Add Server"
select "Command Stdio"
enter "uv" at command to run
enter name of the server for the id
the settings.json file should open
modify the directory path and ensure it is pointing to where you have the server installed
add the args so that it looks like:

Note: you will need to modify the directory path in the args for your system, this needs to be a complete path. You may also need to have a complete path to uv in the command as well.

    "mcp": {
        "servers": {
            "teradataStdio": {
                "type": "stdio",
                "command": "uv",
                "args": [
                    "--directory",
                    "/Users/Daniel.Tehan/Code/MCP/teradata-mcp-server",
                    "run",
                    "teradata-mcp-server"
                ],
                "env": {
                    "DATABASE_URI": "teradata://username:password@host:1025/databasename"
                }
            }
        }
    }

you can start the server from within the settings.json file or you can "MCP: Start Server"

Using with Claude Desktop

You can add this server Claude desktop adding this entry to your claude_desktop_config.json config file:

Note: you will need to modify the directory path in the args for your system, this needs to be a complete path. You may also need to have a complete path to uv in the command as well.

Note: this requires that uv is available to Claude in your system path or installed globally on your system (eg. uv installed with brew for Mac OS users).

{
  "mcpServers": {
    "teradataStdio": {
      "command": "uv",
      "args": [
        "--directory",
        "/path_to_code/teradata-mcp-server",
        "run",
        "teradata-mcp-server"
      ],
      "env": {
        "DATABASE_URI": "teradata://username:password@host:1025/databasename"
      }
    }
  }
}

Using with AI Agents (stdio version)

Option 1 - pydanticai chatbot

step 1 - confirm the MCP_TRANSPORT=stdio in .env file

MCP_TRANSPORT=stdio

Step 2 - Modify the ./test/ClientChatBot.py script to point to where you installed the server, you will need to modify the following line

    td_mcp_server = MCPServerStdio('uv', ["--directory", "/Users/Daniel.Tehan/Code/MCP/teradata-mcp-server", "run", "teradata-mcp-server"])

Step 3 - run the ./test/ClientChatBot.py script, this will create an interactive session with the agent who has access to the MCP server. From a terminal.

uv run ./test/ClientChatBot.py

Ask the agent to list the databases
Ask the agent to list the table in a database
Ask the agent to show all the objects in a database
Ask the agent a question that requires SQL to run against a table
Type "quit" to exit.

Option 2 - ADK Chatbot

step 1 - confirm the MCP_TRANSPORT=stdio in .env file

MCP_TRANSPORT=stdio

Step 2 - move into teradata_mcp_server/test directory From a terminal.

cd test
adk web

Step 3 - open ADK Web Server

Step 4 - chat with the td_agent

Option 3 - mcp_chatbot

step 0 - Modify server_config.json in the test directory, ensure path is correct.

step 1 - confirm the MCP_TRANSPORT=stdio in .env file

MCP_TRANSPORT=stdio

Step 2 - move into teradata_mcp_server directory From a terminal and run the mcp_chatbot

uv run test/mcp_chatbot.py

Step 3 - list the prompts by typing /prompts

Query: /prompts

Step 4 - running a prompt to describe a database

Query: /prompt base_databaseBusinessDesc database_name=demo_user

Using with any tool: REST interface

You can use mcpo to expose this MCP tool as an OpenAPI-compatible HTTP server.

For example, using uv:

uvx mcpo --port 8002 --api-key "top-secret" -- uv run teradata-mcp-server

TOr with Docker, using the "rest" profile:

export MCPO_API_KEY=top-secret
docker compose --profile rest up

Your Teradata tools are now available as local REST endpoints, view documentation and test it at http://localhost:8002/docs

Using with Open WebUI

Open WebUI is user-friendly self-hosted AI platform designed to operate entirely offline, supporting various LLM runners like Ollama. It provides a convenient way to interact with LLMs and MCP servers from an intuitive GUI. It can be integrated with this MCP server using the REST endpoints.

Run the MCP server as a REST server in the section above.

python -m venv ./env
source ./env/bin/activate
pip install open-webui   
open-webui serve

Access the UI at http://localhost:8080. To add the MCP tools, navigate to Settings > Tools > Add Connection, and enter your mcpo server connection details (eg. localhost:8001, password = top-secret if you have executed the command line in the mcpo section).

You should be able to see the tools in the Chat Control Valves section on the right and get your models to use it.

You can now access the OpenAPI docs at: http://localhost:8002/docs

For more details on mcpo, see: https://github.com/open-webui/mcpo

Testing your server with MCP Inspector

Step 1 - Start the server, typer the following in your terminal

uv run mcp dev ./src/teradata_mcp_server/server.py

NOTE: If you are running this on a Windows machine and get npx, npm or node.js errors, install the required node.js software from here: https://github.com/nodists/nodist

Step 2 - Open the MCP Inspector

You should open the inspector tool, go to http://127.0.0.1:6274
Click on tools
Click on list tools
Click on read_database_list
Click on run

Test the other tools, each should have a successful outcome

Control+c to stop the server in the terminal

Certification

Name		Name	Last commit message	Last commit date
Latest commit History 161 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
documentation		documentation
src/teradata_mcp_server		src/teradata_mcp_server
test		test
.gitignore		.gitignore
.python-version		.python-version
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
custom_tools.yaml		custom_tools.yaml
docker-compose.yml		docker-compose.yml
env		env
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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Repository files navigation

Teradata MCP Server

Overview

TLDR; I want to try it locally now

Key features

Available tools and prompts

Adding custom tools

Environment Set Up

Using uv

Using Docker

Run the MCP server with SSE (default)

Run the MCP server with REST

Using with Visual Studio Code Co-pilot

Using Server-Sent Events (SSE) (recommended)

Using stdio

Using with Claude Desktop

Using with AI Agents (stdio version)

Option 1 - pydanticai chatbot

Option 2 - ADK Chatbot

Option 3 - mcp_chatbot

Using with any tool: REST interface

Using with Open WebUI

Testing your server with MCP Inspector

Certification

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Teradata/teradata-mcp-server

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Teradata MCP Server

Overview

TLDR; I want to try it locally now

Key features

Available tools and prompts

Adding custom tools

Environment Set Up

Using uv

Using Docker

Run the MCP server with SSE (default)

Run the MCP server with REST

Using with Visual Studio Code Co-pilot

Using Server-Sent Events (SSE) (recommended)

Using stdio

Using with Claude Desktop

Using with AI Agents (stdio version)

Option 1 - pydanticai chatbot

Option 2 - ADK Chatbot

Option 3 - mcp_chatbot

Using with any tool: REST interface

Using with Open WebUI

Testing your server with MCP Inspector

Certification

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