Mem0 MCP Server

# MCP Server with Mem0 for Managing Coding Preferences This demonstrates a structured approach for using an [MCP](https://modelcontextprotocol.io/introduction) server with [mem0](https://mem0.ai) to manage coding preferences efficiently. The server can be used with Cursor and provides essential tools for storing, retrieving, and searching coding preferences. ## Installation 1. Clone this repository 2. Initialize the `uv` environment: ```bash uv venv ``` 3. Activate the virtual environment: ```bash source .venv/bin/activate ``` 4. Install the dependencies using `uv`: ```bash # Install in editable mode from pyproject.toml uv pip install -e . ``` 5. Update `.env` file in the root directory with your mem0 API key: ```bash MEM0_API_KEY=your_api_key_here ``` ## Usage 1. Start the MCP server: ```bash uv run main.py ``` 2. In Cursor, connect to the SSE endpoint, follow this [doc](https://docs.cursor.com/context/model-context-protocol) for reference: ``` http://0.0.0.0:8080/sse ``` 3. Open the Composer in Cursor and switch to `Agent` mode. ## Demo with Cursor https://github.com/user-attachments/assets/56670550-fb11-4850-9905-692d3496231c ## Features The server provides three main tools for managing code preferences: 1. `add_coding_preference`: Store code snippets, implementation details, and coding patterns with comprehensive context including: - Complete code with dependencies - Language/framework versions - Setup instructions - Documentation and comments - Example usage - Best practices 2. `get_all_coding_preferences`: Retrieve all stored coding preferences to analyze patterns, review implementations, and ensure no relevant information is missed. 3. `search_coding_preferences`: Semantically search through stored coding preferences to find relevant: - Code implementations - Programming solutions - Best practices - Setup guides - Technical documentation ## Why? This implementation allows for a persistent coding preferences system that can be accessed via MCP. The SSE-based server can run as a process that agents connect to, use, and disconnect from whenever needed. This pattern fits well with "cloud-native" use cases where the server and clients can be decoupled processes on different nodes. ### Server By default, the server runs on 0.0.0.0:8080 but is configurable with command line arguments like: ``` uv run main.py --host <your host> --port <your port> ``` The server exposes an SSE endpoint at `/sse` that MCP clients can connect to for accessing the coding preferences management tools.