🚀 Memgraph MCP Server

Memgraph MCP Server is a lightweight server implementation of the Model Context Protocol (MCP) designed to connect Memgraph with LLMs.

⚡ Quick start

📹 Memgraph MCP Server Quick Start video

1. Run Memgraph MCP Server

1. Install uv and create venv with uv venv. Activate virtual environment with .venv\Scripts\activate.
2. Install dependencies: uv add "mcp[cli]" httpx
3. Run Memgraph MCP server: uv run server.py.

2. Run MCP Client

1. Install Claude for Desktop.
2. Add the Memgraph server to Claude config:

MacOS/Linux

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows

code $env:AppData\Claude\claude_desktop_config.json

Example config:

{
    "mcpServers": {
      "mpc-memgraph": {
        "command": "/Users/katelatte/.local/bin/uv",
        "args": [
            "--directory",
            "/Users/katelatte/projects/mcp-memgraph",
            "run",
            "server.py"
        ]
     }
   }
}

Note

You may need to put the full path to the uv executable in the command field. You can get this by running which uv on MacOS/Linux or where uv on Windows. Make sure you pass in the absolute path to your server.

3. Chat with the database

1. Run Memgraph MAGE:

   docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True
   

   The --schema-info-enabled configuration setting is set to True to allow LLM to run SHOW SCHEMA INFO query.
2. Open Claude Desktop and see the Memgraph tools and resources listed. Try it out! (You can load dummy data from Memgraph Lab Datasets)

🔧Tools

run_query()

Run a Cypher query against Memgraph.

🗃️ Resources

get_schema()

Get Memgraph schema information (prerequisite: --schema-info-enabled=True).

🗺️ Roadmap

The Memgraph MCP Server is just at its beginnings. We're actively working on expanding its capabilities and making it even easier to integrate Memgraph into modern AI workflows. In the near future, we'll be releasing a TypeScript version of the server to better support JavaScript-based environments. Additionally, we plan to migrate this project into our central AI Toolkit repository, where it will live alongside other tools and integrations for LangChain, LlamaIndex, and MCP. Our goal is to provide a unified, open-source toolkit that makes it seamless to build graph-powered applications and intelligent agents with Memgraph at the core.