GraphRAG MCP Server

A Model Context Protocol server for querying a hybrid graph and vector database system, combining Neo4j (graph database) and Qdrant (vector database) for powerful semantic and graph-based document retrieval.

Overview

GraphRAG MCP provides a seamless integration between large language models and a hybrid retrieval system that leverages the strengths of both graph databases (Neo4j) and vector databases (Qdrant). This enables:

• Semantic search through document embeddings
• Graph-based context expansion following relationships
• Hybrid search combining vector similarity with graph relationships
• Full integration with Claude and other LLMs through MCP

This project follows the Model Context Protocol specification, making it compatible with any MCP-enabled client.

Features

• Semantic search using sentence embeddings and Qdrant
• Graph-based context expansion using Neo4j
• Hybrid search combining both approaches
• MCP tools and resources for LLM integration
• Full documentation of Neo4j schema and Qdrant collection information

Prerequisites

• Python 3.12+
• Neo4j running on localhost:7687 (default configuration)
• Qdrant running on localhost:6333 (default configuration)
• Document data indexed in both databases

Installation

Quick Start

1. Clone this repository:

   git clone https://github.com/rileylemm/graphrag_mcp.git
   cd graphrag_mcp
   

2. Install dependencies with uv:

   uv install
   

3. Configure your database connections in the .env file:

   # Neo4j Configuration
   NEO4J_URI=bolt://localhost:7687
   NEO4J_USER=neo4j
   NEO4J_PASSWORD=password
   
   # Qdrant Configuration
   QDRANT_HOST=localhost
   QDRANT_PORT=6333
   QDRANT_COLLECTION=document_chunks
   

4. Run the server:

   uv run main.py
   

Detailed Setup Guide

For a detailed guide on setting up the underlying hybrid database system, please refer to the companion repository: GraphRAG Hybrid Database

Setting up Neo4j and Qdrant

1. Install and start Neo4j:

   # Using Docker
   docker run \
     --name neo4j \
     -p 7474:7474 -p 7687:7687 \
     -e NEO4J_AUTH=neo4j/password \
     -v $HOME/neo4j/data:/data \
     -v $HOME/neo4j/logs:/logs \
     -v $HOME/neo4j/import:/import \
     -v $HOME/neo4j/plugins:/plugins \
     neo4j:latest
   

2. Install and start Qdrant:

   # Using Docker
   docker run -p 6333:6333 -p 6334:6334 \
     -v $HOME/qdrant/storage:/qdrant/storage \
     qdrant/qdrant
   

Indexing Documents

To index your documents in both databases, follow these steps:

1. Prepare your documents
2. Create embeddings using sentence-transformers
3. Store documents in Neo4j with relationship information
4. Store document chunk embeddings in Qdrant

Refer to the GraphRAG Hybrid Database repository for detailed indexing scripts and procedures.

Integration with MCP Clients

Claude Desktop / Cursor Integration

1. Make the run script executable:

   chmod +x run_server.sh
   

2. Add the server to your MCP configuration file (~/.cursor/mcp.json or Claude Desktop equivalent):

   {
     "mcpServers": {
       "GraphRAG": {
         "command": "/path/to/graphrag_mcp/run_server.sh",
         "args": []
       }
     }
   }
   

3. Restart your MCP client (Cursor, Claude Desktop, etc.)

Usage

MCP Tools

This server provides the following tools for LLM use:

1. search_documentation - Search for information using semantic search

   # Example usage in MCP context
   result = search_documentation(
       query="How does graph context expansion work?",
       limit=5,
       category="technical"
   )
   

2. hybrid_search - Search using both semantic and graph-based approaches

   # Example u
   

... View full README on GitHub