mcp-neo4j-memory MCP Server: Knowledge Graph Memory Stored in Neo4j

Overview: What is mcp-neo4j-memory MCP Server?

The mcp-neo4j-memory MCP Server stores and retrieves entities and relationships from your personal knowledge graph, enabling seamless integration with a wide range of AI applications. By leveraging this server, developers can manage and access their structured data through natural language commands, enhancing the utility and accessibility of Neo4j's powerful graph database capabilities.

🔧 Core Features & MCP Capabilities

The mcp-neo4j-memory MCP Server offers several key features that make it an indispensable tool for AI applications. These include:

• Real-time Knowledge Storage: It stores entities and relationships, allowing users to update and retrieve data dynamically.
• Cross-Session Accessibility: Enables access to the stored knowledge graph across multiple sessions and clients, ensuring consistency in data management.
• Scalability & Flexibility: Supports both local and remote Neo4j instances, providing flexibility depending on your deployment requirements.

⚙️ MCP Architecture & Protocol Implementation

The mcp-neo4j-memory MCP Server uses Model Context Protocol (MCP) to interact seamlessly with AI applications. The protocol ensures a standardized way of communication between the client and the server, allowing for easy integration and scalability. Below is an excerpt from the MCP protocol flow diagram:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Neo4j Database]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

This diagram illustrates the flow of communication between an AI application (through MCP Client), the MCP Protocol, and the mcp-neo4j-memory MCP Server interfacing with a Neo4j database.

🚀 Getting Started with Installation

To get started with the mcp-neo4j-memory MCP Server, follow these steps:

1. Clone the Repository: git clone https://github.com/neo4j/mcp-neo4j-memory.git
2. Install Dependencies: Navigate to the repository directory and run npm install.
3. Configure Environment Variables: Set up environment variables in your .env file or command-line arguments.
4. Start the Server: Run the server using node index.js.

💡 Key Use Cases in AI Workflows

Use Case 1: Personal Knowledge Base Management

A user can utilize the mcp-neo4j-memory MCP Server to store personal notes and professional data, facilitating easy recall and collaboration. For example, a developer working on a project might input facts about the project's architecture, team members, and deadlines.

Use Case 2: Data-Driven Decision Making

In an enterprise setting, this server could be used for storing customer data, product information, and sales analytics. By querying the knowledge graph, business intelligence analysts can make informed decisions based on real-time data insights.

🔌 Integration with MCP Clients

The mcp-neo4j-memory MCP Server is compatible with a variety of MCP clients, including:

• Claude Desktop: Fully supports natural language interaction and dynamic queries.
• Continue: Offers robust support for entity management but lacks command-level prompt functionality.
• Cursor: Only provides limited access to the Neo4j knowledge graph.

The following compatibility matrix details the current status of each client with the server:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Limited Support
Cursor	❌	✅	❌	Basic Integration

📊 Performance & Compatibility Matrix

The performance of the mcp-neo4j-memory MCP Server is designed to handle large datasets efficiently, ensuring fast and reliable data retrieval. The compatibility matrix below outlines its support for various AI tools:

Tool	Supported
Neo4j Aura	✅
AWS	❌
Azure	❌

🛠️ Advanced Configuration & Security

Advanced users can configure the mcp-neo4j-memory server by customizing environment variables and security settings. An example configuration snippet is provided below:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "API_KEY": "your-api-key"
      }
    }
  }
}

This JSON snippet defines the MCP server configuration, including its command to start, arguments, and environment variables needed for secure connection.

❓ Frequently Asked Questions (FAQ)

1. Q: How can I integrate my existing Neo4j Aura instance with this server?

   • A: Ensure your Neo4j Aura instance is properly configured with the correct database URI in the server settings.

2. Q: Does Claude Desktop fully support entity management through MCP servers?

   • A: Yes, but it requires additional configuration for optimal performance.

3. Q: Can I use this server with other tools than Neo4j Aura?

   • A: At present, only limited support is available for AWS and Azure storage solutions.

4. Q: Is there any specific security measure one should take when using this server?

   • A: Yes, ensure to use secure API keys and configure proper environment variables for authentication.

5. Q: Are there any known compatibility issues with Continue MCP clients?

   • A: While supported, the integration may experience some limitations compared to full support in other clients like Claude Desktop.

👨‍💻 Development & Contribution Guidelines

Contributions are always welcome! To contribute, follow these steps:

1. Fork the Repository: Create a fork of this repository on GitHub.
2. Create a Pull Request (PR): Make your changes and ensure to write clear commit messages.
3. Run Tests: Before submitting a PR, run the tests using npm test to ensure everything works as expected.
4. Document Your Changes: If necessary, update documentation files to reflect new features or bug fixes.

🌐 MCP Ecosystem & Resources

For more information on Model Context Protocol (MCP) and related resources, visit:

• Model Context Protocol Documentation
• Neo4j Developer Blog
• Official MCP Client Compatibility Matrix

By leveraging the mcp-neo4j-memory server, developers and AI enthusiasts can harness the power of Neo4j's knowledge graph to enhance their workflows and drive more effective problem-solving through natural language integration.