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Watsonx RAG MCP Server logo

Watsonx RAG MCP Server

ruslanmv/watsonx-rag-mcp-server

In this tutorial, you will learn how to build a full-featured Retrieval-Augmented Generation (RAG) server using IBM Watsonx.ai, ChromaDB for vector indexing, and expose it via the Model Context Protocol (MCP) Python SDK.

View on GitHub1

Install

docker run --rm -p 5001:5001 \

Client configuration

{
  "mcpServers": {
    "rag_chatbot": {
      "command": "python3",
      "args": [
        "/absolute/path/to/your_project/server.py"
      ]
    }
  }
}

Environment variables

WATSONX_APIKEYWATSONX_URLPROJECT_ID
GitHub stars
1
Author
@ruslanmv
Category
Knowledge Management, AI Chatbot
Updated
Oct 6, 2026

About Watsonx RAG MCP Server

By the end, you’ll have a standalone server that processes PDF documents and a client to make queries against them, leveraging the power of large language models grounded in your specific data. At the end we shows how to integrate directly inside Claude Desktop.

The Model Context Protocol (MCP) standardizes the interface between applications and LLMs. With MCP, you can separate the concerns of providing context, executing code, and managing user interactions. The MCP Python SDK implements the full MCP specification, allowing you to:

Tools (3)

  • Retrieve

    It uses pdf_collection.query() to find the NUM_RESULTS_RAG document chunks whose vector embeddings are closest (most similar) to the vector embedding of the…

  • Augment

    It checks if any documents were found.

  • Generate

Details on this page are taken from the project's README. Open README

Supported clients

Clients mentioned in this server's README:

View all
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Anthropic's official Claude AI desktop application. Supports MCP servers to extend functionality.

WindowsMacOS

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