cognee-mcp-server: An MCP Server for AI Memory Engine Integration

Overview: What is cognee-mcp-server?

cognee-mcp-server is an advanced Model Context Protocol (MCP) server that facilitates seamless integration between AI applications and the cognee memory engine. This server enables AI applications to connect to a wide range of data sources and tools through a standardized protocol, making it easier for developers to build versatile and effective AI workflows.

🔧 Core Features & MCP Capabilities

cognee-mcp-server is designed with several core features that significantly enhance the capabilities of MCP clients. These features include:

1. Knowledge Graph Construction: The server can process input text to create a knowledge graph, making it easier for AI applications to retrieve and use contextual information.
2. Search Functionality: It supports searching within the constructed knowledge graph using specified queries.
3. Custom Graph Model Support: Users can provide custom pydantic graph model implementations for more tailored integration needs.

Knowledge Graph Construction

The Cognify_and_search tool builds a knowledge graph from the input text, allowing AI applications to understand and utilize this structured data effectively. Inputs include:

• text (String): The context used to construct the knowledge graph.
• search_query (String): The query used for searching within the constructed graph.
• Optional Parameters:
  • graph_model_file (String): A filename of a custom pydantic graph model implementation.
  • graph_model_name (String): The class name of a custom pydantic graph model implementation.

Search Functionality

Once the knowledge graph is constructed, users can perform searches within it using specified queries. The tool returns retrieved edges from the knowledge graph as output.

⚙️ MCP Architecture & Protocol Implementation

The cognee-mcp-server implements the Model Context Protocol (MCP) to ensure compatibility with various AI applications. By conforming to this protocol, developers can create versatile and interoperable systems that leverage different data sources and tools through consistent interfaces.

Protocol Flow Diagram

To illustrate how cogeene-mcp-server interacts with other components in an MCP ecosystem, consider the following Mermaid diagram:

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

This diagram shows the flow of communication between an AI application, the MCP client, the MCP protocol server, and external data sources or tools.

🚀 Getting Started with Installation

To install cognee-mcp-server, follow these steps:

1. Ensure you have Node.js and npm installed.
2. Clone the repository from GitHub:

   git clone https://github.com/your-repository/cognee-mcp-server.git
   

3. Navigate to the project directory:

   cd cognee-mcp-server
   

4. Install dependencies using npm:

   npm install
   

5. Run the server with the necessary environment variables configured.

💡 Key Use Cases in AI Workflows

cognee-mcp-server enables a wide range of use cases, some of which are illustrated below:

Use Case 1: Context-Aware Chatbots

Imagine a chatbot assistant that uses cognee-mcp-server to understand and answer user queries based on contextual information. The bot can build context from previous conversations, search through this knowledge graph for relevant data, and provide answers seamlessly.

Use Case 2: Data Retrieval for ML Models

A machine learning model could leverage the constructed knowledge graph to retrieve specific pieces of data, ensuring that it has access to the most pertinent and accurate information when making predictions or decisions.

🔌 Integration with MCP Clients

The cognee-mcp-server is compatible with a variety of MCP clients. Below are some notable integrations:

• Claude Desktop: Full support for chat and context retrieval.
• Continue: Support includes tools integration but not prompts directly.
• Cursor: Supported primarily as a tool provider.

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

The compatibility matrix provides detailed information on which clients are fully supported and to what extent:

• Full Support: Clients that can fully utilize the features provided by the server.
• Partial Support: Clients with some utility but not all aspects available.

The specific tools and resources each client supports is outlined in this matrix, helping developers choose the most suitable server for their needs.

🛠️ Advanced Configuration & Security

To ensure robust security and performance of the cognee-mcp-server, users can configure it as follows:

Configuration Example

{
  "mcpServers": {
    "cogneeServer": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-cognee"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration block demonstrates how to set up the cognee-mcp-server with environment variables for API key protection and other necessary settings.

❓ Frequently Asked Questions (FAQ)

Q: Can I use multiple MCP servers simultaneously?

A: Yes, you can configure your project to use multiple MCP servers by adding more entries in the mcpServers section of your configuration file.

Q: Are there any specific security measures I should take when using the server?

A: It's recommended to set up environment variables like API_KEY and ensure that sensitive information is encrypted or managed securely.

Q: Which MCP clients are currently supported, and how do they integrate with cognee-mcp-server?

A: The server has full support for Claude Desktop and Continue while providing tools integration for Cursor. Specific configurations are provided in the README to facilitate these integrations.

Q: How does the knowledge graph construction impact performance?

A: While building a robust knowledge graph, additional processing time may be required, but this can be optimized by choosing appropriate data models and input text preprocessing techniques.

Q: Can I integrate custom tools with cognee-mcp-server?

A: Yes, you can provide custom implementations of your pydantic graph models to enhance the tool integrations and make them more specific to your needs.

👨‍💻 Development & Contribution Guidelines

Contributions to cognee-mcp-server are welcome. Developers interested in contributing should follow these guidelines:

1. Fork the repository from GitHub.
2. Clone the repository on your local machine.
3. Make your changes and ensure they pass tests.
4. Submit a pull request with detailed descriptions of your modifications.

🌐 MCP Ecosystem & Resources

The Model Context Protocol (MCP) forms part of a broader ecosystem that includes various AI applications, servers, and tools. Developers interested in learning more about MCP can visit the official documentation or community forums for additional resources and support.

By integrating cognee-mcp-server into your AI application, you unlock a world of possibilities where data sources and tools are easily accessible and fully leveraged within your software solutions. This server acts as a bridge that enhances interoperability between different components in complex AI workflows, making it an invaluable tool in the development process.