RAG MCP Server

MCP Server that gives Claude Code semantic search over your PDFs, code, and documents. Index once, query instantly — with exact citations and zero hallucinations.

Setup

1. Install

git clone https://github.com/Rubrum95/rag-mcp-server
cd rag-mcp-server
pip install .

For OCR support (scanned PDFs):

pip install ".[ocr]"

# macOS
brew install tesseract

# Windows — download installer from:
# https://github.com/UB-Mannheim/tesseract/wiki

# Linux
sudo apt install tesseract-ocr tesseract-ocr-spa

2. Connect to Claude Code

Add to ~/.claude/settings.json:

{
  "mcpServers": {
    "rag": {
      "command": "rag-mcp-server"
    }
  }
}

Usage

Index a project

Ask Claude: "Index ~/projects/my-research"
→ Calls rag_index, processes all PDFs and code files

Query documents

Ask Claude: "What does the paper say about ocean warming?"
→ Calls rag_query, returns exact text with page citations

Update with new files

Ask Claude: "Update the my-research index"
→ Calls rag_update, only processes new/changed files

List indexed projects

Ask Claude: "List my indexed projects"
→ Shows all projects with file/chunk counts

Configuration

Copy config.yaml to ~/.rag-mcp-server/config.yaml to customize:

• embedding_model — default: multilingual model (Spanish + English)
• chunk_size / chunk_overlap — text splitting parameters
• top_k — default number of search results
• ocr_languages — Tesseract languages for scanned PDFs
• supported_extensions — file types to index

How It Works

Your files → Text extraction → Chunking → Embeddings → ChromaDB
                (+ OCR if needed)

Your question → Embedding → Cosine similarity search → Top chunks
                                                          ↓
                                            Claude reads exact text
                                            and responds with citations

Requirements

• Python 3.10+
• ~500MB disk for embedding model (downloaded once)
• Tesseract (optional, for scanned PDFs)