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MCP Server with Mem0 for Managing Coding Preferences

This demonstrates a structured approach for using an MCP server with mem0 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:
uv venv
  1. Activate the virtual environment:
source .venv/bin/activate
  1. Install the dependencies using uv:
# Install in editable mode from pyproject.toml
uv pip install -e .

Setup

Before proceeding, ensure you have created a .env file in the root directory with the following content:

MEM0_API_KEY=<your-api-key>
HOST=<your-host> # Optional, defaults to 0.0.0.0
PORT=<your-port> # Optional, defaults to 8080

Replace <your-api-key>, <your-host>, and <your-port> with your actual values. These variables are required for the application to function correctly, except HOST and PORT, which have default values.

Usage

  1. Start the MCP server:
uv run main.py
  1. In Cursor, connect to the SSE endpoint, follow this doc for reference:
http://0.0.0.0:8080/sse
  1. Open the Composer in Cursor and switch to Agent mode.

Using Docker

To run the MCP server with Docker, follow these steps:

  1. Build and start the Docker container using docker-compose:
docker-compose up --build -d

This command will build the Docker image and start the container in detached mode.

  1. Verify that the container is running:
docker ps

You should see the mem0-server container listed.

  1. Access the MCP server at the configured endpoint:
http://<your-host>:<your-port>/sse

Replace <your-host> and <your-port> with the values you set in the .env file.

  1. To stop the container, use:
docker-compose down

This will stop and remove the container.

Demo with Cursor

mcp_demo.mp4

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.