Prompt Ops MCP

# Prompt Ops MCP A streamlined Model Context Protocol (MCP) server that optimizes prompts using meta-prompting techniques. This server can be easily integrated into Cursor and other MCP-compatible tools to enhance prompt quality and effectiveness. ## Features - **Two-Turn Prompt Optimization**: Transform basic prompts into sophisticated, structured requests using a simple two-turn approach - **Meta-Prompting Technique**: Leverages the LLM's capabilities to apply optimization guidelines - **MCP Integration**: Seamlessly integrates with Cursor and other MCP-compatible tools - **TypeScript**: Built with TypeScript for type safety and better development experience ## Installation ### Via NPM (Recommended) ```bash npm install -g prompt-ops-mcp ``` ### From Source ```bash git clone <repository-url> cd prompt-ops-mcp npm install npm run build ``` ## Usage ### Integration with Cursor Add the following to your Cursor MCP settings: ```json { "mcpServers": { "prompt-optimizer": { "command": "npx", "args": ["prompt-ops-mcp"] } } } ``` ### Direct Usage ```bash # Run the server npx prompt-ops-mcp # Or if installed globally prompt-ops-mcp ``` ## How It Works: Two-Turn Optimization The prompt optimizer uses a simple two-turn approach: 1. **Turn 1**: Provide your original prompt → Receive optimization guidelines 2. **Turn 2**: Provide the optimized prompt → Get it ready for use ### Available Tool: `promptenhancer` **Parameters:** - `originalPrompt`: The prompt you want to optimize (for Turn 1) - `optimizedPrompt`: The optimized prompt created by following the guidelines (for Turn 2) **Example Usage (Turn 1):** ``` @prompt-ops promptenhancer {"originalPrompt": "Write a Python function to calculate fibonacci numbers"} ``` **Example Usage (Turn 2):** ``` @prompt-ops promptenhancer {"optimizedPrompt": "Your optimized prompt here..."} ``` ## Optimization Guidelines The meta-prompting framework includes guidance for: 1. **Clarifying Intent and Scope**: Making implicit requirements explicit 2. **Adding Structure and Organization**: Breaking complex requests into clear sections 3. **Enhancing with Reasoning Elements**: Including step-by-step thinking instructions 4. **Providing Context and Examples**: Adding relevant background information 5. **Setting Quality Standards**: Defining success criteria and constraints ## Example Transformation See [example-two-turn.md](example-two-turn.md) for a complete example of the two-turn optimization process. ## Development ### Setup ```bash git clone <repository-url> cd prompt-ops-mcp npm install ``` ### Development Scripts ```bash # Run in development mode npm run dev # Build the project npm run build # Run tests npm run test # Lint code npm run lint # Format code npm run format ``` ### Project Structure ``` src/ ├── index.ts # Main MCP server implementation ├── prompt-optimizer.ts # Core prompt optimization logic └── types.ts # TypeScript type definitions ``` ## Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Add tests for new functionality 5. Run `npm run lint` and `npm run format` 6. Submit a pull request ## License MIT License - see LICENSE file for details ## Support For issues and questions: - GitHub Issues: [Create an issue](https://github.com/yourusername/prompt-ops-mcp/issues) - Discussions: [Join the discussion](https://github.com/yourusername/prompt-ops-mcp/discussions) ## Changelog ### v1.0.0 - Initial release with two-turn prompt optimization - Full MCP integration support