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Building a Watsonx.ai Chatbot Server with MCP in Python

@ruslanmv

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AI とエージェント

トランスポート

stdio

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ruslanmv

Building a Watsonx.ai Chatbot Server with MCP in Python について

How to create a professional, production‑ready chatbot server powered by IBM Watsonx.ai and exposed via the Model Context Protocol (MCP) Python SDK.

Building a Watsonx.ai Chatbot Server with MCP in Python の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "watsonx-mcp-server": {
      "command": "python3",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Building a Watsonx.ai Chatbot Server with MCP in Python のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Watsonx Chatbot Server?

Watsonx Chatbot Server is a production‑ready MCP server that exposes IBM Watsonx.ai LLM inference as a standardized “chat” tool. It lets any MCP‑compatible client (e.g., Claude Desktop, custom Python clients) invoke Watsonx.ai responses through the Model Context Protocol.

How to use Watsonx Chatbot Server?

Set up Python 3.9+, a virtual environment, and install python-dotenv, ibm-watsonx-ai, and mcp[cli]. Store your Watsonx.ai API key, URL, and project ID in a .env file. Run mcp dev server.py for live‑reloading development with an Inspector UI at http://localhost:6274/, or run python server.py for direct STDIO mode. Use the sample client script to call the chat tool.

Key features of Watsonx Chatbot Server

  • Exposes Watsonx.ai LLM inference as an MCP “chat” tool.
  • Provides a dynamic greeting resource via @mcp.resource.
  • Includes an optional symptom‑assessment prompt template.
  • Supports live reloading and Inspector testing with mcp dev.
  • Communicates over STDIO transport with any MCP client.
  • Defaults to the ibm/granite-13b-instruct-v2 model.

Use cases of Watsonx Chatbot Server

  • Building an internal helpdesk bot that responds to employee queries.
  • Creating a public chatbot API that works with any MCP‑aware frontend.
  • Rapidly prototyping medical symptom assessment assistants.
  • Providing a reusable, modular LLM endpoint for multiple client applications.

よくある質問

1

What credentials are required to run the server?

You need an IBM Cloud Watsonx.ai API key, service URL, and project ID. Store them in a `.env` file as `WATSONX_APIKEY`, `WATSONX_URL`, and `PROJECT_ID`.

2

How do I install the dependencies?

Use `pip install -r requirements.txt` after creating a virtual environment. The required packages are `python-dotenv>=0.21.0`, `ibm-watsonx-ai==1.3.8`, and `mcp[cli]>=1.6.0`.

3

What transport does the server use?

The server uses STDIO transport for MCP communication. It runs as a command‑line process and exchanges JSON‑RPC messages over standard input/output.

4

How can I test the server without writing a client?

Run `mcp dev server.py` to start the development mode. Open `http://localhost:6274/` in a browser to use the MCP Inspector for listing tools, calling the `chat` tool, and testing resources and prompts.

5

What model is used by default, and can I change it?

The default model is `ibm/granite-13b-instruct-v2`. You can override it by setting the `MODEL_ID` environment variable in your `.env` file.

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