watsonx-mcp-server
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This document provides a comprehensive guide to building a Watsonx.ai Chatbot Server using the Model Context Protocol (MCP) in Python.
The tutorial outlines the process of creating a professional, production-ready chatbot server powered by IBM Watsonx.ai and exposed via the Model Context Protocol (MCP) Python SDK. The server is designed to be reusable and can be invoked by any MCP-compatible client, such as Claude Desktop or custom Python clients. The guide covers setting up the environment, installing dependencies, writing clean Python code, exposing Watsonx.ai inference as an MCP tool, and running and testing the server. It also provides tips for extending and hardening the service. The tutorial emphasizes the modularity, reusability, and rapid iteration capabilities of combining IBM Watsonx.ai with MCP, making it suitable for building scalable and adaptable chatbot solutions.
Features
- Modularity: Decouple chatbot logic from client implementations.
- Reusability: Any MCP-compatible client can call the same 'chat' endpoint.
- Rapid iteration: Built-in development inspector with live reloading.
- Secure credentials management using environment variables.
- Integration with IBM Watsonx.ai for LLM inference.
Tools
chat
Generates a chatbot response via Watsonx.ai.
