RAG MCP Server

A standalone Model Context Protocol (MCP) server that exposes semantic document retrieval — over a ChromaDB collection of arXiv research papers — as a callable tool for any MCP-compatible client (Claude Desktop, custom agents, etc.).

This project decouples the retrieval layer from the Agentic Corrective RAG System (a separate project), so the same indexed knowledge base can be queried by any LLM client without needing to know about ChromaDB, embeddings, or the underlying pipeline.

MCP Client (Claude Desktop / any MCP-compatible agent)
        │   MCP protocol (stdio)
        ▼
RAG MCP Server (this project)
        │
        ▼
retrieve_documents(query, top_k)
        │
        ▼
ChromaDB collection "research_papers"
(allenai-specter embeddings, dense retrieval)

Why this exists

Most RAG systems bury retrieval inside a single monolithic pipeline — the vector store is only reachable by running the whole application end to end. This project takes just the retrieval component and puts a standard interface in front of it, so:

• Any MCP client can query the knowledge base directly, without touching LangGraph, Groq, or any generation/correction logic
• The vector store becomes reusable infrastructure instead of an implementation detail locked inside one app
• Swapping the underlying vector DB (ChromaDB → Qdrant → Pinecone) would require no changes to any client, only to this server

What it exposes

retrieve_documents(query: str, top_k: int = 5)

Performs dense semantic search over a corpus of arXiv research papers and returns structured, typed results — not a raw text blob.

Returns: a list of RetrievedChunk objects:

Field	Type	Description
content	str	The retrieved chunk text
title	str	Title of the source paper
source	str	Source PDF filename
page	int	Page number within the source document
arxiv_id	str	arXiv identifier / URL for the paper
score	float	Similarity distance score (lower = closer match)

Example call (via an MCP client):

"Use retrieve_documents to find information about attention mechanisms in transformers"

Example structured output:

[
  {
    "content": "Quantifying attention flow in transformers...",
    "title": "Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA",
    "source": "2607.15241v1.pdf",
    "page": 6,
    "arxiv_id": "https://arxiv.org/abs/2607.15241v1",
    "score": 0.42
  }
]

Architecture notes

• Embeddings: sentence-transformers/allenai-specter, run locally — no API key or embedding cost.
• Vector store: ChromaDB, loaded read-only from a pre-built persisted collection (research_papers). This server never re-indexes or re-embeds documents — it only queries an existing index built by the source RAG pipeline.
• Transport: stdio, following the standard local MCP server pattern used by Claude Desktop.
• Server framework: FastMCP, Anthropic's Python SDK for MCP servers.

Setup

1. Install dependencies

uv add mcp langchain-huggingface langchain-chroma sentence-transformers

2. Provide the vector store

This server expects a pre-built, persisted ChromaDB collection at ./data/chroma_db with collection name research_papers. If you're using this against your own document set, build that collection first using your own ingestion pipeline, or point CHROMA_PERSIST_DIR in vectorstore.py at your existing persisted collection.

3. Run the server directly (sanity check)

uv run python server.py

No output is expected — the server sits idle over stdio waiting for a client to connect. This is normal.

4. Connect to Claude Desktop

Add the server to your claude_desktop_config.json:

{
  "mcpServers": {
    "rag-retrieval-server": {
      "command": "C:\\path\\to\\uv.exe",
      "args": [
        "--directory",
        "C:\\path\\to\\rag-mcp-server",
        "run",
        "python",
        "server.py"
      ]
    }
  }
}

Fully quit and restart Claude Desktop, then check "+" → Connectors in the chat box to confirm rag-retrieval-server is listed with the retrieve_documents tool.

Project structure

rag-mcp-server/
├── server.py          # MCP server + tool definitions
├── vectorstore.py      # Loads the existing Chroma collection (read-only)
├── data/
│   └── chroma_db/      # Persisted ChromaDB collection (copied from source project)
└── README.md

Relationship to the Corrective RAG project

This server reuses the exact same indexed document collection as the Agentic Corrective RAG System — same embeddings, same ChromaDB store — but strips away the LangGraph orchestration, relevance grading, web-search fallback, and Groq-based generation. It exposes only the retrieval primitive, as a standalone, protocol-compliant service.

Possible extensions

• retrieve_by_paper(arxiv_id, query) — scoped search within a single paper
• list_indexed_papers() — enumerate all papers currently in the collection
• HTTP/SSE transport, so the server is reachable over a network instead of only local stdio