🌉 mcp-rag-bridge

Connect any AI agent to your document knowledge base via MCP (Model Context Protocol).

This server wraps a RAG (Retrieval-Augmented Generation) pipeline as MCP tools — so Claude, Qwen Code, or any MCP client can query your documents, add new ones, and get grounded answers with citations.

Why this exists

"A RAG pipeline without an agent interface is just a search engine. An agent without RAG is just a chatbot. The bridge makes both smarter."

This project connects the two worlds: your documents become the agent's knowledge, and the agent becomes the interface to that knowledge.

Related MCP server: Modular RAG MCP Server

Architecture

┌─────────────────────┐
│   MCP Client (AI)   │
│  Claude / Qwen Code │
└──────────┬──────────┘
           │ stdio (JSON-RPC)
┌──────────▼──────────┐
│   MCP RAG Bridge    │
│                     │
│  query_kb ──────────────┐
│  add_document ─────┐    │
│  list_sources ──┐  │    │
│  delete_doc ─┐  │  │    │
│  search ──┐  │  │  │    │
└──────────┼──┼──┼──┼────┘
           │  │  │  │
     ┌─────▼──▼──▼──▼────┐
     │   ChromaDB +      │
     │   Embeddings      │
     └───────────────────┘

Tools (6)

Quick start

git clone https://github.com/zyay/mcp-rag-bridge.git
cd mcp-rag-bridge
python -m venv venv && venv\Scripts\activate
pip install -r requirements.txt

# Start the MCP server
python server.py

# Or test with the included client
python client_test.py

Connect an MCP client

Qwen Code

qwen mcp add rag-bridge -- python /full/path/to/mcp-rag-bridge/server.py

Claude Code

claude mcp add rag-bridge -- python /full/path/to/mcp-rag-bridge/server.py

Claude Desktop

{
  "mcpServers": {
    "rag-bridge": {
      "command": "python",
      "args": ["/full/path/to/mcp-rag-bridge/server.py"]
    }
  }
}

Usage examples

Once connected, the agent can interact with your knowledge base naturally:

User: "What documents do you know about?"
Agent: [calls list_sources()]

User: "Index the file at ./docs/architecture.md"
Agent: [calls add_document("./docs/architecture.md")]

User: "How does our authentication system work?"
Agent: [calls query_knowledge_base("authentication system", use_llm=True)]

User: "Find documents similar to 'database migration strategy'"
Agent: [calls search_similar("database migration strategy")]

LLM generation

Set use_llm=True in query_knowledge_base to get grounded answers:

• Ollama (default): Local, free, no API key. Set OLLAMA_MODEL=llama3.2.

• OpenAI fallback: Set LLM_PROVIDER=openai and OPENAI_API_KEY=sk-...

The bridge tries the primary provider first, then falls back automatically.

Environment variables

Design decisions

Relationship to other projects

• rag-docs-assistant — Full RAG pipeline with web UI, evals, reranking. This project shares the same core concepts but is self-contained.

• mcp-agent-tools — General-purpose MCP tools (file, MySQL, web, calc). This project is the RAG-specific counterpart.

Together, these three projects demonstrate a complete AI agent ecosystem: general tools + knowledge retrieval + the bridge between them.

License

MIT