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memgraph

Memgraph MCP Server

Official
by memgraph
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Python
Hybrid

[！重要的]
该存储库已合并到Memgraph AI Toolkit monorepo 中，以避免重复工具。
它将在一个月内被删除 - 请关注那里的MCP 集成以进行所有未来开发，并随时在该 repo 中打开问题或 PR。

🚀 Memgraph MCP 服务器

Memgraph MCP 服务器是模型上下文协议 (MCP) 的轻量级服务器实现，旨在将 Memgraph 与 LLM 连接起来。

mcp服务器

⚡ 快速启动

📹 Memgraph MCP 服务器快速入门视频

1. 运行 Memgraph MCP 服务器

  1. 安装uv并使用uv venv创建venv 。使用.venv\Scripts\activate虚拟环境。

  2. 安装依赖项： uv add "mcp[cli]" httpx

  3. 运行 Memgraph MCP 服务器： uv run server.py 。

2. 运行 MCP 客户端

  1. 安装Claude 桌面版。

  2. 将 Memgraph 服务器添加到 Claude 配置：

MacOS/Linux

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

视窗

code $env:AppData\Claude\claude_desktop_config.json

示例配置：

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

[！笔记]
您可能需要在命令字段中输入 uv 可执行文件的完整路径。您可以在 MacOS/Linux 上运行which uv或在 Windows 上where uv来获取此路径。请确保传入的是服务器的绝对路径。

3. 与数据库聊天

  1. 运行 Memgraph MAGE：

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

    --schema-info-enabled配置设置设为True以允许 LLM 运行SHOW SCHEMA INFO查询。

  2. 打开 Claude Desktop，查看列出的 Memgraph 工具和资源。快来试试吧！（您可以从Memgraph Lab数据集加载虚拟数据）

Related MCP server: mcp-graphql

🔧工具

运行查询（）

对 Memgraph 运行 Cypher 查询。

🗃️ 资源

获取模式（）

获取 Memgraph 模式信息（先决条件： --schema-info-enabled=True ）。

🗺️ 路线图

Memgraph MCP 服务器才刚刚起步。我们正在积极扩展其功能，使其更容易集成到现代 AI 工作流程中。不久的将来，我们将发布该服务器的 TypeScript 版本，以更好地支持基于 JavaScript 的环境。此外，我们计划将该项目迁移到我们的中央AI 工具包存储库，它将与 LangChain、LlamaIndex 和 MCP 的其他工具和集成共存。我们的目标是提供一个统一的开源工具包，使以 Memgraph 为核心构建基于图驱动的应用程序和智能代理变得无缝衔接。

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.

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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

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  • MCP Database Server

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