This project provides Neo4j integration for the Model Context Protocol (MCP), enabling users to interact with Neo4j databases and Neo4j Aura cloud services using natural language via MCP-compatible clients. It targets developers and users who want to leverage LLMs for data analysis, knowledge graph management, and cloud resource orchestration within the Neo4j ecosystem.

How It Works

The project consists of several components: mcp-neo4j-cypher translates natural language into Cypher queries for Neo4j, mcp-neo4j-memory stores and retrieves entities and relationships in Neo4j for knowledge graph applications, and mcp-neo4j-cloud-aura-api manages Neo4j Aura instances. This modular approach allows for flexible integration with various LLM clients and use cases.

Quick Start & Requirements

• Install: pip install mcp-neo4j (specific components may require separate installation or configuration).
• Prerequisites: Neo4j instance (local or Aura), Python 3.x.
• Resources: Requires access to a Neo4j database and potentially Neo4j Aura API keys.
• Docs: Blog Posts provide context and usage examples.

Highlighted Details

• Enables natural language querying of Neo4j databases.
• Facilitates building and querying knowledge graphs stored in Neo4j.
• Allows management of Neo4j Aura cloud instances via AI assistants.
• Supports integration with MCP clients like Claude Desktop, VS Code, and Cursor.

Maintenance & Community

• Contributions are welcome via Pull Requests.
• Associated with Neo4j, a major graph database vendor.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive MIT license allows for commercial use and integration into closed-source applications.

Limitations & Caveats

The project is presented as a set of integrations rather than a standalone application, requiring users to have an existing MCP client and a Neo4j instance. Specific setup details for each component are primarily within the README.