Teradata MCP Server Template

This code will form the basis for building Teradata MCP servers.

We have provided a base code under the /teradata_mcp_server directory that can be modified for the development of custom tools

Environment Set Up

Step 1 - The environment has been put together assuming you have the uv package installed on your local machine. Installation instructions for uv can be found at https://github.com/astral-sh/uv

Step 2 - Clone the mcp-server repository with

On Windows

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

On Mac

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

Step 3 - You need to update the .env file

• Rename env file to .env

• The database URI will have the following format teradata://username:password@host:1025/databasename, use a ClearScape Analytics Experience https://www.teradata.com/getting-started/demos/clearscape-analytics

  • the usename needs updating
  • the password needs updating
  • the Teradata host needs updating
  • the databasename needs updating

• LLM Credentials need to be available for /test/pydanticaiBedrock.py code to work

• SSE setting

  • SSE : Boolean to determine if your server will be using the SSE transport (SSE = True) or the stdio transport (SSE=False)
  • SSE_HOST: IP address that the server can be found at, default should be 127.0.0.1
  • SSE_PORT: Port address that the server can be fount at, default should be 8001

Example .env file

############################################
DATABASE_URI=teradata://username:password@host:1025/databasename
SSE=False
SSE_HOST=127.0.0.1
SSE_PORT=8001

############################################
aws_access_key_id=
aws_secret_access_key=
aws_session_token=
aws_region_name=

############################################
OPENAI_API_KEY=

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

Adding your sever to an Agent using stdio

step 1 - confirm the SSE flag in .env file has been set to False

SSE=False

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/src/teradata_mcp_server", "run", "server.py"])

Step 2 - 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.

Adding tools using stdio to Visual Studio Code Co-pilot

• confirm the SSE flag in .env file has been set to False

SSE=False

• In VS Code, "Show and Run Commands"
• 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:

    "mcp": {
        "servers": {
            "TeradataStdio": {
                "type": "stdio",
                "command": "uv",
                "args": [
                    "--directory",
                    "/Users/Daniel.Tehan/Code/MCP/teradata-mcp-server/src/teradata_mcp_server",
                    "run",
                    "server.py"
                ]
            }
        }
    }

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

Adding tools using SSE to Visual Studio Code Co-pilot

• confirm the SSE flag in .env file has been set to False

SSE=True
SSE_HOST=127.0.0.1
SSE_PORT=8001

• you need to start the server from a terminal

uv run ./src/teradata_mcp_server/server.py

• In VS Code, "Show and Run Commands"
• 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"

Exposing tools as REST endpoints with mcpo

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

For example, using uv: uvx mcpo --port 8001 --api-key "top-secret" -- uv run src/teradata_mcp_server/server.py

Your Teradata tools are now available as local REST endpoints, view documentation and test it at http://localhost:8001/docs

Using the server 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 mcpo component.

First run mcpo as specified 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.