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memgraph

Memgraph MCP Server

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Python
Hybrid
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

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

mcp-server

⚡ 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)

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 tool
run_queryC

Run a query against Memgraph

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose if this is read-only or mutating, what permissions are needed, error handling, or performance implications (e.g., timeouts, rate limits). This leaves significant gaps for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, direct sentence with no wasted words—it's front-loaded and appropriately sized for a simple tool. Every word earns its place by stating the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't cover behavioral traits, parameter details, or return values, making it inadequate for a tool that likely executes database operations with potential side effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds no parameter semantics beyond the schema's 'query' field. It doesn't explain what the query should contain (e.g., syntax, format), expected inputs, or constraints, failing to compensate for the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Run a query against Memgraph' clearly states the action (run) and target (Memgraph), but it's vague about what type of query (Cypher? SQL?) and what resources are affected. Without sibling tools, differentiation isn't needed, but the purpose remains somewhat generic.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool—there are no alternatives mentioned, no context for usage, and no prerequisites or exclusions. The description assumes the agent knows when to run queries without any framing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedrun_query
Install Server
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TDQS

C2.8/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness1/5

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.

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • mcp-graphql

    launchthatbrand
    A
    license
    Not graded
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to interact with GraphQL APIs by providing schema introspection and query execution capabilities.
    1,317
    3
    MIT

  • MCP Database Server

    manpreet2000
    A
    license
    D
    quality
    D
    maintenance
    A Model Context Protocol server that enables LLMs to interact with databases (currently MongoDB) through natural language, supporting operations like querying, inserting, deleting documents, and running aggregation pipelines.
    5
    14
    MIT

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