Content
# 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.
MCP Store
