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Building a Watsonx.ai Chatbot Server with MCP in Python

@ruslanmv

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

ruslanmv

About Building a Watsonx.ai Chatbot Server with MCP in Python

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 config

Add this server to your MCP-compatible client using the configuration below.

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

Building a Watsonx.ai Chatbot Server with MCP in Python tools

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Overview

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.

Frequently asked questions

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.

Comments

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