mem0 MCP Server

What does “mem0” MCP Server do?

The mem0 MCP (Model Context Protocol) Server is designed to manage coding preferences efficiently by connecting AI assistants with a structured system for storing, retrieving, and searching code snippets and related development context. Acting as a middleware, it allows AI clients to interact with external data—such as code implementations, setup instructions, documentation, and best practices—through standardized tools and endpoints. Its main role is to streamline development workflows by enabling features like semantic search, persistent storage of coding guidelines, and retrieval of comprehensive programming patterns, which can be integrated into AI-powered IDEs or coding agents. This enhances both individual and team productivity by making best practices and reusable code easily accessible.

List of Prompts

No prompt templates are mentioned in the repository or documentation.

List of Resources

No explicit MCP resources are listed in the repository or documentation.

List of Tools

• add_coding_preference: Stores code snippets, implementation details, and coding patterns, along with context such as dependencies, versions, setup instructions, and example usage.
• get_all_coding_preferences: Retrieves all stored coding preferences for analysis, review, and ensuring completeness.
• search_coding_preferences: Performs semantic search across stored coding preferences to find relevant implementations, solutions, best practices, and technical documentation.

Use Cases of this MCP Server

• Persistent Coding Preferences Storage: Developers can save intricate coding preferences, including dependencies, language versions, and setup instructions, ensuring knowledge retention over time.
• Semantic Search for Code and Patterns: Users can perform advanced searches to quickly locate relevant code snippets, setup guides, and best practices, improving onboarding and team consistency.
• Review and Analysis of Coding Implementations: Teams can retrieve all saved coding patterns for code review, pattern analysis, or to ensure best practices are being followed.
• Integration with AI-Powered IDEs: The server can be connected to tools like Cursor, enabling AI agents to suggest, retrieve, or update coding preferences directly within the development environment.
• Documentation Reference and Technical Assistance: Enables LLMs or coding agents to fetch detailed documentation and usage examples, streamlining developer support and reducing manual searching.

How to set it up

Windsurf

1. Ensure you have Python and uv installed on your system.
2. Clone the mem0-mcp repository and install dependencies as per the Installation section.
3. Update your .env file with your MEM0 API key.
4. Add the mem0 MCP server configuration to your Windsurf setup:

{
  "mcpServers": {
    "mem0-mcp": {
      "command": "uv",
      "args": ["run", "main.py"],
      "env": {
        "MEM0_API_KEY": "${MEM0_API_KEY}"
      }
    }
  }
}

5. Save the configuration, restart Windsurf, and verify the server is running.

Note: Secure your API key using environment variables, as shown in the env section above.

Claude

1. Follow the repository’s installation instructions to set up the server locally.
2. Locate Claude’s MCP server configuration file.
3. Add the mem0 MCP server with a JSON snippet like:

{
  "mcpServers": {
    "mem0-mcp": {
      "command": "uv",
      "args": ["run", "main.py"],
      "env": {
        "MEM0_API_KEY": "${MEM0_API_KEY}"
      }
    }
  }
}

4. Save and restart Claude to load the MCP server.
5. Confirm connectivity and tool exposure.

Note: Use environment variables for sensitive data.

Cursor

1. Clone and install the mem0-mcp as per the README.
2. Set your MEM0 API key in the .env file.
3. Start the server with uv run main.py.
4. In Cursor, connect to the SSE endpoint (http://0.0.0.0:8080/sse).
5. Open the Composer in Cursor and switch to Agent mode.

JSON Configuration Example:

{
  "mcpServers": {
    "mem0-mcp": {
      "command": "uv",
      "args": ["run", "main.py"],
      "env": {
        "MEM0_API_KEY": "${MEM0_API_KEY}"
      }
    }
  }
}

Note: Store your API key securely using environment variables.

Cline

1. Set up Python and dependencies as described in the installation section.
2. Place your MEM0 API key in the .env file.
3. Add the MCP server configuration to Cline’s mcpServers object:

{
  "mcpServers": {
    "mem0-mcp": {
      "command": "uv",
      "args": ["run", "main.py"],
      "env": {
        "MEM0_API_KEY": "${MEM0_API_KEY}"
      }
    }
  }
}

4. Save and restart Cline.
5. Verify the mem0 MCP server is accessible and functional.

Note: Use environment variables for API key management.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "mem0-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “mem0-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the available information, mem0-mcp provides clear tool definitions and setup instructions but lacks explicit prompt templates and resource definitions, and does not document advanced MCP features like roots or sampling. As a result, it is functional but basic in terms of protocol completeness.

MCP Score