by memgraph
Provides a lightweight server implementation of the Model Context Protocol to enable Memgraph graph database interaction with large language models.
Enables Memgraph to receive and execute Cypher queries from LLMs via the Model Context Protocol, exposing query and schema endpoints as tools for AI agents.
uv venv && .venv\Scripts\activate (Linux/macOS: source .venv/bin/activate).3. Install dependencies: uv add "mcp[cli]" httpx.4. Start the server: uv run server.py.docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=True
--schema-info-enabled=True.server.py script.Q: Do I need a Memgraph license?
A: The server works with both the open‑source community edition and the enterprise edition of Memgraph.
Q: Which ports does the server listen on?
A: By default it uses the port defined by the underlying MCP library (commonly 8000); you can adjust it via environment variables in the server script.
Q: Can I run the server in production?
A: Yes, but for production you should containerize the server, enable TLS, and manage the virtual environment with a process manager.
Q: Is there a TypeScript version?
A: A TypeScript implementation is planned for a future release and will be part of the Memgraph AI Toolkit monorepo.
[!IMPORTANT]
This repository has been merged into the Memgraph AI Toolkit monorepo to avoid duplicating tools.
It will be deleted in one month—please follow the MCP integration there for all future development, and feel free to open issues or PRs in that repo.
Memgraph MCP Server is a lightweight server implementation of the Model Context Protocol (MCP) designed to connect Memgraph with LLMs.

uv and create venv with uv venv. Activate virtual environment with .venv\Scripts\activate.uv add "mcp[cli]" httpxuv run server.py.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 runningwhich uvon MacOS/Linux orwhere uvon Windows. Make sure you pass in the absolute path to your server.
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.Run a Cypher query against Memgraph.
Get Memgraph schema information (prerequisite: --schema-info-enabled=True).
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.
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{
"mcpServers": {
"memgraph-mcp-server": {
"command": "uv",
"args": [
"run",
"server.py"
],
"env": {}
}
}
}claude mcp add memgraph-mcp-server uv run server.pyExplore related MCPs that share similar capabilities and solve comparable challenges
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