Memgraph MCP Server
OfficialThe Memgraph MCP Server acts as a bridge between Memgraph and LLMs, enabling AI-driven database interactions through a lightweight implementation. With this server, you can:
Run queries: Execute Cypher queries against Memgraph using the
run_query()toolAccess schema information: Retrieve database structure details with the
get_schema()resource (requires--schema-info-enabled=True)Interact conversationally: Connect LLMs like Claude to interact with your database directly from a chat interface
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Memgraph MCP Server show me all users who purchased product X in the last month"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
Memgraph MCP Server is a lightweight server implementation of the Model Context Protocol (MCP) designed to connect Memgraph with LLMs.

⚡ Quick start
1. Run Memgraph MCP Server
Install
uvand createvenvwithuv venv. Activate virtual environment with.venv\Scripts\activate.Install dependencies:
uv add "mcp[cli]" httpxRun Memgraph MCP server:
uv run server.py.
2. Run MCP Client
Install Claude for Desktop.
Add the Memgraph server to Claude config:
MacOS/Linux
code ~/Library/Application\ Support/Claude/claude_desktop_config.jsonWindows
code $env:AppData\Claude\claude_desktop_config.jsonExample config:
{
"mcpServers": {
"mpc-memgraph": {
"command": "/Users/katelatte/.local/bin/uv",
"args": [
"--directory",
"/Users/katelatte/projects/mcp-memgraph",
"run",
"server.py"
]
}
}
}
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
Run Memgraph MAGE:
docker run -p 7687:7687 memgraph/memgraph-mage --schema-info-enabled=TrueThe
--schema-info-enabledconfiguration setting is set toTrueto allow LLM to runSHOW SCHEMA INFOquery.Open Claude Desktop and see the Memgraph tools and resources listed. Try it out! (You can load dummy data from Memgraph Lab Datasets)
Related MCP server: mcp-graphql
🔧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.
Available Tools
1 toolrun_queryC
Run a query against Memgraph
Inputs: query (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'run_query' follows a clear verb_noun pattern.
One tool is too few for a database server's apparent scope, as it severely limits functionality (e.g., no schema management, data manipulation beyond queries, or connection handling). This is a significant mismatch for the domain.
The tool surface is severely incomplete for a database server. It only supports running queries, lacking essential operations like creating/dropping databases, managing schemas, listing tables, or handling transactions, which will cause frequent agent failures.
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Repository knowledge graph MCP server for codebase understanding and debugging.
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