GraphRAG MCP Server
@rileylemm
About GraphRAG MCP Server
This is a MCP server I built to interact with my hybrid graph rag db.
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"graphrag_mcp": {
"command": "uv",
"args": [
"run",
"main.py"
]
}
}
}Tools
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Overview
What is GraphRAG MCP Server?
A Model Context Protocol (MCP) server that queries a hybrid graph and vector database system combining Neo4j (graph database) and Qdrant (vector database). Built for developers integrating large language models with powerful semantic and graph-based document retrieval.
How to use GraphRAG MCP Server?
Clone the repository, install dependencies with uv install, configure Neo4j and Qdrant connection details in a .env file, then run uv run main.py. For MCP client integration, add the server to your mcp.json configuration file (e.g., for Claude Desktop or Cursor) pointing to the run_server.sh script.
Key features of GraphRAG MCP Server
- Semantic search through document embeddings
- Graph-based context expansion following relationships
- Hybrid search combining vector similarity with graph links
- MCP tools and resources for LLM integration
- Full documentation of Neo4j schema and Qdrant collection info
Use cases of GraphRAG MCP Server
- Enabling LLMs to perform semantic document retrieval
- Expanding context using graph relationships for richer answers
- Hybrid search that balances vector similarity with structured graph queries
- Integrating Neo4j‑ and Qdrant‑backed knowledge into Claude Desktop or Cursor
FAQ from GraphRAG MCP Server
What are the runtime requirements?
Python 3.12+, plus a running Neo4j instance on localhost:7687 and a running Qdrant instance on localhost:6333 (default ports). Documents must be already indexed in both databases.
How do I install the server?
Clone the repository, run uv install to install dependencies, set database credentials in .env, then start with uv run main.py. A run_server.sh script is provided for MCP client registration.
Why am I getting empty results?
Ensure your document collection is properly indexed in both Neo4j and Qdrant. Verify database connectivity and that the QDRANT_COLLECTION name in .env matches your indexed collection.
How do I configure authentication for Neo4j and Qdrant?
Set NEO4J_USER, NEO4J_PASSWORD, and optionally NEO4J_URI (default bolt://localhost:7687) in .env. Qdrant authentication is not explicitly covered; the default configuration assumes no auth.
What transports and authentication does the server use?
The server uses the MCP protocol over HTTP/WebSocket (native). Neo4j communicates via Bolt protocol, Qdrant via HTTP. Credentials for Neo4j are read from environment variables. No MCP‑level auth is described.
Frequently asked questions
What are the runtime requirements?
Python 3.12+, plus a running Neo4j instance on localhost:7687 and a running Qdrant instance on localhost:6333 (default ports). Documents must be already indexed in both databases.
How do I install the server?
Clone the repository, run `uv install` to install dependencies, set database credentials in `.env`, then start with `uv run main.py`. A `run_server.sh` script is provided for MCP client registration.
Why am I getting empty results?
Ensure your document collection is properly indexed in both Neo4j and Qdrant. Verify database connectivity and that the `QDRANT_COLLECTION` name in `.env` matches your indexed collection.
How do I configure authentication for Neo4j and Qdrant?
Set `NEO4J_USER`, `NEO4J_PASSWORD`, and optionally `NEO4J_URI` (default `bolt://localhost:7687`) in `.env`. Qdrant authentication is not explicitly covered; the default configuration assumes no auth.
What transports and authentication does the server use?
The server uses the MCP protocol over HTTP/WebSocket (native). Neo4j communicates via Bolt protocol, Qdrant via HTTP. Credentials for Neo4j are read from environment variables. No MCP‑level auth is described.
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