This is a MCP server I built to interact with my hybrid graph rag db.
{
"mcpServers": {
"graphrag-mcp": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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This is a MCP server I built to interact with my hybrid graph rag db.
Is it safe?
No package registry to scan.
No authentication — any process on your machine can connect.
MIT. View license →
Is it maintained?
Last commit 370 days ago. 57 stars.
Will it work with my client?
Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
No automated test available for this server. Check the GitHub README for setup instructions.
No known vulnerabilities.
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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.
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:
This project follows the Model Context Protocol specification, making it compatible with any MCP-enabled client.
Clone this repository:
git clone https://github.com/rileylemm/graphrag_mcp.git
cd graphrag_mcp
Install dependencies with uv:
uv install
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
Run the server:
uv run main.py
For a detailed guide on setting up the underlying hybrid database system, please refer to the companion repository: GraphRAG Hybrid Database
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
Install and start Qdrant:
# Using Docker
docker run -p 6333:6333 -p 6334:6334 \
-v $HOME/qdrant/storage:/qdrant/storage \
qdrant/qdrant
To index your documents in both databases, follow these steps:
Refer to the GraphRAG Hybrid Database repository for detailed indexing scripts and procedures.
Make the run script executable:
chmod +x run_server.sh
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": []
}
}
}
Restart your MCP client (Cursor, Claude Desktop, etc.)
This server provides the following tools for LLM use:
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"
)
hybrid_search - Search using both semantic and graph-based approaches
# Example u