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memgraph

Memgraph MCP Server

Official
by memgraph
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

[!중요한]
이 저장소는 도구 중복을 피하기 위해 Memgraph AI Toolkit 모노레포에 병합되었습니다.
한 달 후에 삭제될 예정입니다. 향후 모든 개발에 대해서는 해당 MCP 통합을 따르시고, 해당 저장소에서 문제나 PR을 자유롭게 열어 주시기 바랍니다.

🚀 Memgraph MCP 서버

Memgraph MCP 서버는 Memgraph를 LLM과 연결하도록 설계된 Model Context Protocol(MCP)의 가벼운 서버 구현입니다.

mcp 서버

⚡ 빠른 시작

📹 Memgraph MCP 서버 빠른 시작 비디오

1. Memgraph MCP 서버 실행

  1. uv 설치하고 uv venv 로 venv 생성하세요. .venv\Scripts\activate 로 가상 환경을 활성화하세요.

  2. 종속성 설치: uv add "mcp[cli]" httpx

  3. Memgraph MCP 서버를 실행합니다: uv run server.py .

2. MCP 클라이언트 실행

  1. 데스크톱에 Claude를 설치하세요.

  2. Claude 구성에 Memgraph 서버를 추가합니다.

맥OS/리눅스

지엑스피1

윈도우

code $env:AppData\Claude\claude_desktop_config.json

구성 예:

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

[!메모]
명령 필드에 uv 실행 파일의 전체 경로를 입력해야 할 수도 있습니다. MacOS/Linux에서는 which uv , Windows에서는 where uv 실행하여 경로를 확인할 수 있습니다. 서버의 절대 경로를 입력해야 합니다.

3. 데이터베이스와 채팅

  1. Memgraph MAGE를 실행하세요:

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

    --schema-info-enabled 구성 설정이 True 로 설정되어 LLM이 SHOW SCHEMA INFO 쿼리를 실행할 수 있습니다.

  2. Claude Desktop을 열고 Memgraph 도구와 리소스 목록을 확인하세요. 직접 사용해 보세요! ( Memgraph Lab 데이터셋에서 더미 데이터를 불러올 수 있습니다.)

Related MCP server: mcp-graphql

🔧도구

실행_쿼리()

Memgraph에 대해 Cypher 쿼리를 실행합니다.

🗃️ 리소스

get_schema()

Memgraph 스키마 정보를 가져옵니다(필수 조건: --schema-info-enabled=True ).

🗺️ 로드맵

Memgraph MCP 서버는 아직 초기 단계입니다. 저희는 서버 기능을 확장하고 Memgraph를 최신 AI 워크플로에 더욱 쉽게 통합할 수 있도록 적극적으로 노력하고 있습니다. 가까운 시일 내에 JavaScript 기반 환경을 더욱 효과적으로 지원하기 위해 TypeScript 버전의 서버를 출시할 예정입니다. 또한, 이 프로젝트를 중앙 AI 툴킷 저장소로 이전하여 LangChain, LlamaIndex, MCP용 다른 도구 및 통합 기능과 함께 사용할 계획입니다. 저희의 목표는 Memgraph를 핵심으로 그래프 기반 애플리케이션과 지능형 에이전트를 원활하게 구축할 수 있도록 하는 통합 오픈소스 툴킷을 제공하는 것입니다.

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
    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.
    913 npm
    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
    6 npm
    MIT

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