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GraphRAG MCP Server

@rileylemm

リポジトリ

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

rileylemm

GraphRAG MCP Server について

This is a MCP server I built to interact with my hybrid graph rag db.

#graphdb#vector-db#mcp-server

GraphRAG MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "graphrag_mcp": {
      "command": "uv",
      "args": [
        "run",
        "main.py"
      ]
    }
  }
}

GraphRAG MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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.

2

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.

3

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.

4

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.

5

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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