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)
npm install -g prompt-ops-mcp
From Source
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:
{
"mcpServers": {
"prompt-optimizer": {
"command": "npx",
"args": ["prompt-ops-mcp"]
}
}
}
Direct Usage
# 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:
- Turn 1: Provide your original prompt → Receive optimization guidelines
- 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:
- Clarifying Intent and Scope: Making implicit requirements explicit
- Adding Structure and Organization: Breaking complex requests into clear sections
- Enhancing with Reasoning Elements: Including step-by-step thinking instructions
- Providing Context and Examples: Adding relevant background information
- Setting Quality Standards: Defining success criteria and constraints
Example Transformation
See example-two-turn.md for a complete example of the two-turn optimization process.
Development
Setup
git clone <repository-url>
cd prompt-ops-mcp
npm install
Development Scripts
# 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
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests for new functionality
- Run
npm run lintandnpm run format - Submit a pull request
License
MIT License - see LICENSE file for details
Support
For issues and questions:
- GitHub Issues: Create an issue
- Discussions: Join the discussion
Changelog
v1.0.0
- Initial release with two-turn prompt optimization
- Full MCP integration support