# MDC Rules Generator

> **Disclaimer:** This project is not officially associated with or endorsed by Cursor. It is a community-driven initiative to enhance the Cursor experience.

This project generates Cursor MDC (Markdown Cursor) rule files from a structured JSON file containing library information. It uses Exa for semantic search and LLM (Gemini) for content generation.

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## Features

- Generates comprehensive MDC rule files for libraries
- Uses Exa for semantic web search to gather best practices
- Leverages LLM to create detailed, structured content
- Supports parallel processing for efficiency
- Tracks progress to allow resuming interrupted runs
- Smart retry system that focuses on failed libraries by default

## Prerequisites

- Python 3.8+
- [uv](https://github.com/astral-sh/uv) for dependency management
- API keys for:
  - Exa (for semantic search)
  - LLM provider (Gemini, OpenAI, or Anthropic)

## Installation

1. Clone this repository:
   ```bash
   git clone https://github.com/sanjeed5/awesome-cursor-rules-mdc.git
   cd awesome-cursor-rules-mdc
   ```

2. Install dependencies using uv:
   ```bash
   uv sync
   ```

3. Set up environment variables:
   Create a `.env` file in the project root with your API keys (see `.env.example`):
   ```
   EXA_API_KEY=your_exa_api_key
   GEMINI_API_KEY=your_google_gemini_api_key  # For Gemini
   # Or use one of these depending on your LLM choice:
   # OPENAI_API_KEY=your_openai_api_key
   # ANTHROPIC_API_KEY=your_anthropic_api_key
   ```

## Usage

Run the generator script with:

```bash
uv run src/generate_mdc_files.py
```

By default, the script will only process libraries that failed in previous runs.

### Command-line Options

- `--test`: Run in test mode (process only one library)
- `--tag TAG`: Process only libraries with a specific tag
- `--library LIBRARY`: Process only a specific library
- `--output OUTPUT_DIR`: Specify output directory for MDC files
- `--verbose`: Enable verbose logging
- `--workers N`: Set number of parallel workers
- `--rate-limit N`: Set API rate limit calls per minute
- `--regenerate-all`: Process all libraries, including previously completed ones

### Examples

```bash
# Process failed libraries (default behavior)
uv run src/generate_mdc_files.py

# Regenerate all libraries
uv run src/generate_mdc_files.py --regenerate-all

# Process only Python libraries
uv run src/generate_mdc_files.py --tag python

# Process a specific library
uv run src/generate_mdc_files.py --library react
```

## Adding New Rules

Adding support for new libraries is simple:

1. **Edit the rules.json file**:
   - Add a new entry to the `libraries` array:
   ```json
   {
     "name": "your-library-name",
     "tags": ["relevant-tag1", "relevant-tag2"]
   }
   ```

2. **Generate the MDC files**:
   - Run the generator script:
   ```bash
   uv run src/generate_mdc_files.py
   ```
   - The script automatically detects and processes new libraries

3. **Contribute back**:
   - Test your new rules with real projects
   - Consider raising a PR to contribute your additions back to the community

## Configuration

The script uses a `config.yaml` file for configuration. You can modify this file to adjust:

- API rate limits
- Output directories
- LLM model selection
- Processing parameters

## Project Structure

```
.
├── src/                  # Main source code directory
│   ├── generate_mdc_files.py  # Main generator script
│   ├── config.yaml       # Configuration file
│   ├── mdc-instructions.txt   # Instructions for MDC generation
│   ├── logs/             # Log files directory
│   └── exa_results/      # Directory for Exa search results
├── rules-mdc/            # Output directory for generated MDC files
├── rules.json            # Input file with library information
├── pyproject.toml        # Project dependencies and metadata
├── .env.example          # Example environment variables
└── LICENSE               # MIT License
```

## License

[MIT License](LICENSE)