Neo4j MCP Server
Neo4j MCP サーバー
モデル コンテキスト プロトコルを通じてグラフ データベース操作を管理するための Neo4j MCP サーバー実装。
🔌 カーソルとクロードデスクトップの両方と互換性があります。
クイックスタート
npx を使用してサーバーを直接実行できます。
# Using a single connection string
NEO4J_CONNECTION=neo4j+s://your-instance.databases.neo4j.io,neo4j,your-password npx neo4j-mcpserver
# Or using separate environment variables
NEO4J_URI=neo4j+s://your-instance.databases.neo4j.io NEO4J_USER=neo4j NEO4J_PASSWORD=your-password npx neo4j-mcpserverRelated MCP server: M.I.M.I.R - Multi-agent Intelligent Memory & Insight Repository
インストール
パッケージをグローバルにインストールする場合:
npm install -g neo4j-mcpserver次にそれを実行します:
NEO4J_CONNECTION=neo4j+s://your-instance.databases.neo4j.io,neo4j,your-password neo4j-mcpserver環境変数
Neo4j 接続の詳細は、次の 2 つの方法で提供できます。
単一の接続文字列を使用する:
NEO4J_CONNECTION=<uri>,<user>,<password>個別の環境変数を使用する:
NEO4J_URI=<your-uri> NEO4J_USER=<your-user> NEO4J_PASSWORD=<your-password>
プロジェクト ルートで.envファイルを使用することもできます。
NEO4J_URI=neo4j+s://your-instance.databases.neo4j.io
NEO4J_USER=neo4j
NEO4J_PASSWORD=your-password設定 ⚙️
カーソルの設定 🖥️
Cursor で Neo4j MCP サーバーを設定するには:
カーソル設定を開く
機能 > MCP サーバーに移動します
「+新しいMCPサーバーを追加」ボタンをクリックします。
以下の情報を入力してください。
名前: サーバーのニックネームを入力します (例: "neo4j-mcp")
タイプ: タイプとして「コマンド」を選択します
コマンド: サーバーを実行するコマンドを入力してください: GXP7
重要: 資格情報を実際の Neo4j データベース資格情報に置き換えてください。
利用可能なツール 🛠️
neo4jクエリ
Neo4j データベースに対して Cypher クエリを実行します。
カーソルでの使用例:
MATCH (n) RETURN n LIMIT 5トラブルシューティング🔧
問題が発生した場合:
Neo4j の資格情報を確認する
Neo4j URI、ユーザー名、パスワードが正しいことを確認してください
Neo4jデータベースにアクセスできることを確認する
パスの問題
インストールパスにスペースがないことを確認してください
パスにはスラッシュ(/)を使用する
ツール検出の問題
カーソルを再起動してみてください
サーバーが実行中であることを確認します(CursorのMCPサーバーリストを確認してください)
環境変数が正しく設定されているか確認する
開発👩💻
ローカルで実行するには:
git clone <repository-url>
cd neo4j-mcpserver
npm install
npm run build
npm startライセンス
ISC
Available Tools
1 toolneo4j-queryC
Execute a Cypher query against the Neo4j database
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The Cypher query to execute | |
| parameters | No | Query parameters (optional) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'Execute' implies a write operation, the description doesn't clarify whether this tool can perform read-only queries, mutations, or both. It lacks information about permissions required, transaction handling, result formats, or potential side effects like data modification or performance impacts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for a straightforward tool and is front-loaded with the essential information. Every word earns its place in this minimal description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a database query execution tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect, error handling, security considerations, or whether queries are read-only or can modify data. The combination of a powerful database tool with minimal description creates significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds no additional parameter information beyond what's in the schema. This meets the baseline expectation when schema coverage is complete, but doesn't provide extra value like Cypher syntax examples or parameter format guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Execute') and target resource ('Cypher query against the Neo4j database'), making the purpose immediately understandable. It lacks sibling differentiation, but since there are no sibling tools on this server, this doesn't reduce clarity. The description avoids tautology by specifying what type of query and database are involved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It simply states what the tool does without context about appropriate use cases. With no sibling tools, the need for differentiation is reduced, but general usage context is still missing.
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 tool update
- First observed
neo4j-query
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clear purpose: executing Cypher queries against a Neo4j database.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'neo4j-query' follows a clear and descriptive pattern, though no pattern can be established or broken with a single tool.
A single tool is too few for a database server's apparent scope, which typically requires operations like creating, updating, deleting, and querying data. This minimal set limits functionality and forces all operations through a generic query interface, which is insufficient for structured interactions.
The tool surface is severely incomplete for a Neo4j database server. While 'neo4j-query' allows executing arbitrary Cypher queries, it lacks dedicated tools for common operations like creating nodes, updating relationships, or managing transactions, leaving significant gaps that agents must work around with raw queries.
Maintenance
Related MCP Connectors
Intelligent context infrastructure for AI teams: knowledge graph, sessions, tasks, documents.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
Cross-agent artifact workspace with provenance across Claude Code, Codex, Cursor, LangGraph.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with persistent graph-based memory capabilities using Neo4j, enabling semantic search, relationship tracking, and knowledge organization across multiple project contexts.402 npm31MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.14 npm287MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to interact with Neo4j graph databases through natural language, supporting Cypher queries, schema management, data manipulation, and graph algorithms.MIT
- FlicenseNot gradedqualityDmaintenanceEnables interaction with Neo4j databases from the Cursor IDE by executing Cypher queries, managing connections, and retrieving database information.3-