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mem0-mcp编码偏好管理器

一个与mem0.ai集成的MCP服务器,帮助用户存储、检索和搜索编程偏好,以实现更一致的编程实践。

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README

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 .
  1. Update .env file in the root directory with your mem0 API key:
MEM0_API_KEY=your_api_key_here

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.

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.

help

运行方式说明

cloud

托管运行

托管运行通常表示这个 MCP Server 由服务方环境承载,用户一般按页面提供的连接方式或授权流程接入,不需要在本地长期启动一个 MCP 进程

  1. 打开服务方连接页
  2. 完成授权或复制端点
  3. 在 MCP 客户端中连接
terminal

本地运行 / 其它方式

本地运行通常需要用户在自己的电脑或服务器上安装依赖,把 server_config 复制到 MCP 客户端,并按 env_schema 补齐环境变量、密钥或其它配置

  1. 复制 server_config
  2. 安装所需依赖
  3. 补齐环境变量后重启客户端