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Building a Watsonx.ai Chatbot RAG Server with MCP Server

@ruslanmv

Repository

Basic information

Category

Memory & Knowledge

Transports

stdio

Author

ruslanmv

About Building a Watsonx.ai Chatbot RAG Server with MCP Server

Building a Watsonx.ai Chatbot RAG Server with MCP compatible with Claude Desktop.

Building a Watsonx.ai Chatbot RAG Server with MCP Server config

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

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

Building a Watsonx.ai Chatbot RAG Server with MCP Server tools

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Overview

What is Building a Watsonx.ai Chatbot RAG Server with MCP?

A Retrieval-Augmented Generation (RAG) server that uses IBM Watsonx.ai for LLM inference, ChromaDB for vector indexing, and exposes a chat tool via the Model Context Protocol (MCP) Python SDK. It processes PDF documents and answers queries based on their content, and can integrate with Claude Desktop.

How to use Building a Watsonx.ai Chatbot RAG Server with MCP?

Set up a Python 3.8+ virtual environment, install dependencies, create a .env file with Watsonx.ai credentials, place PDF files in the documents/ folder, then run server.py to start the MCP server. A client.py script is provided for testing queries, and the server can also be plugged into Claude Desktop.

Key features of Building a Watsonx.ai Chatbot RAG Server with MCP

  • Uses IBM Watsonx.ai for large language model inference
  • Indexes PDF documents in ChromaDB vector database
  • Exposes a single MCP tool (chat_with_manual) for RAG queries
  • Supports configurable chunk size and overlap for text splitting
  • Integrates directly with Claude Desktop via MCP

Use cases of Building a Watsonx.ai Chatbot RAG Server with MCP

  • Answering questions from technical manuals (e.g., drone documentation)
  • Building a chatbot grounded in a company’s internal PDF documents
  • Enabling Claude Desktop to retrieve information from local PDF files
  • Rapid prototyping of a RAG pipeline without custom infrastructure

Frequently asked questions

1

What are the runtime requirements?

Python 3.8+ and an IBM Cloud account with provisioned Watsonx.ai services. You need a Watsonx API key and project ID.

2

Where are the documents and vector data stored?

PDFs are kept in the local `documents/` folder (configurable via `DOCS_FOLDER`). The ChromaDB vector index persists to the `chroma_db_data/` directory.

3

How does the RAG process work?

When a user asks a question, the server retrieves relevant text chunks from ChromaDB, concatenates them with the query, and sends the combined prompt to Watsonx.ai for answer generation.

4

What transport does the MCP server use?

The server is built with FastMCP, which defaults to stdio transport; it is configured to work with Claude Desktop’s MCP integration.

5

Is authentication required?

Yes, the server reads `WATSONX_APIKEY`, `WATSONX_URL`, and `PROJECT_ID` from a `.env` file and validates them at startup.

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