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memgraph

Memgraph MCP Server

Official
by memgraph
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

[!IMPORTANTE]
Este repositorio se ha fusionado con el monorepositorio Memgraph AI Toolkit para evitar la duplicación de herramientas.
Se eliminará en un mes. Siga la integración de MCP allí para todo el desarrollo futuro y no dude en abrir problemas o solicitudes de relaciones públicas en ese repositorio.

Servidor Memgraph MCP

Memgraph MCP Server es una implementación de servidor liviana del Protocolo de contexto de modelo (MCP) diseñado para conectar Memgraph con LLM.

servidor mcp

⚡ Inicio rápido

Vídeo de inicio rápido del servidor Memgraph MCP

1. Ejecute el servidor Memgraph MCP

  1. Instalar uv y crear venv con uv venv . Activar el entorno virtual con .venv\Scripts\activate .

  2. Instalar dependencias: uv add "mcp[cli]" httpx

  3. Ejecutar el servidor Memgraph MCP: uv run server.py .

2. Ejecute el cliente MCP

  1. Instalar Claude para escritorio .

  2. Agregue el servidor Memgraph a la configuración de Claude:

MacOS/Linux

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

Ventanas

code $env:AppData\Claude\claude_desktop_config.json

Ejemplo de configuración:

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

[!NOTA]
Es posible que necesites introducir la ruta completa del ejecutable uv en el campo de comandos. Puedes obtenerla ejecutando which uv en macOS/Linux o where uv en Windows. Asegúrate de introducir la ruta absoluta a tu servidor.

3. Chatea con la base de datos

  1. Ejecutar Memgraph MAGE:

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

    La opción de configuración --schema-info-enabled se establece en True para permitir que LLM ejecute la consulta SHOW SCHEMA INFO .

  2. Abre Claude Desktop y consulta la lista de herramientas y recursos de Memgraph. ¡Pruébalo! (Puedes cargar datos ficticios desde los conjuntos de datos de Memgraph Lab ).

Related MCP server: mcp-graphql

🔧Herramientas

ejecutar_consulta()

Ejecute una consulta Cypher contra Memgraph.

🗃️ Recursos

obtener_esquema()

Obtener información del esquema de Memgraph (requisito previo: --schema-info-enabled=True ).

🗺️ Hoja de ruta

El servidor Memgraph MCP está en sus inicios. Trabajamos activamente para ampliar sus capacidades y facilitar aún más la integración de Memgraph en los flujos de trabajo de IA modernos. Próximamente, lanzaremos una versión TypeScript del servidor para una mejor compatibilidad con entornos basados en JavaScript. Además, planeamos migrar este proyecto a nuestro repositorio central de herramientas de IA , donde se integrará con otras herramientas e integraciones para LangChain, LlamaIndex y MCP. Nuestro objetivo es proporcionar un conjunto de herramientas unificado y de código abierto que facilite la creación de aplicaciones basadas en grafos y agentes inteligentes con Memgraph como núcleo.

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.

  1. 1 tool updatev1.0.0
    • First observedrun_query
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.8/5.0

Scored across 1 tool

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

Related MCP Connectors

Related MCP Servers

  • mcp-graphql

    launchthatbrand
    A
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    quality
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    913 npm
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  • MCP Database Server

    manpreet2000
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    license
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    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.
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