🚀 Prompt Optimizer MCP

A Model Context Protocol (MCP) server that provides intelligent tools for optimizing and scoring LLM prompts using deterministic heuristics.

🎯 Overview

The Prompt Optimizer MCP server offers two powerful tools:

1. optimize_prompt - Generate 3 optimized variants of a raw LLM prompt in different styles
2. score_prompt - Evaluate the effectiveness of an improved prompt relative to the original

Perfect for developers, content creators, and AI practitioners who want to improve their prompt engineering workflow.

✨ Features

🎨 Prompt Optimization Styles

• Creative: Enhanced with descriptive adjectives and engaging language
• Precise: Concise and focused, removing redundant words
• Fast: Optimized for quick processing with shorter synonyms

📊 Intelligent Scoring Algorithm

The scoring system evaluates prompts based on:

• Length optimization (40%): Prefers shorter, more concise prompts
• Keyword preservation (30%): Maintains important terms from the original
• Clarity improvement (30%): Reduces redundancy and improves structure

🔧 Technical Features

• ✅ Stateless: No external dependencies or state management
• ✅ Deterministic: Same inputs always produce same outputs
• ✅ Error-free: Comprehensive input validation and error handling
• ✅ Fast: Simple heuristics for quick processing
• ✅ Extensible: Easy to add new styles and scoring metrics
• ✅ Dual Transport: Supports both STDIO (MCP) and HTTP (deployment)

📁 Project Structure

prompt-optimizer-mcp/
├── 📄 README.md              # This file
├── 📄 server.py              # Main MCP server (STDIO transport)
├── 📄 http_server.py         # HTTP server for deployment
├── 📄 start.py               # Startup script (auto-detects mode)
├── 📄 requirements.txt       # Python dependencies
├── 📄 test_server.py         # Test script
├── 📄 deploy.py              # Deployment script
├── 📄 Dockerfile             # Container configuration
├── 📄 .gitignore             # Git ignore rules
├── 📁 tools/
│   ├── 📄 __init__.py        # Package initialization
│   └── 📄 optimize.py        # Core optimization logic
├── 📁 tests/
│   ├── 📄 __init__.py        # Test package initialization
│   └── 📄 test_optimize.py   # Unit tests
└── 📁 .github/
    └── 📁 workflows/
        └── 📄 ci.yml         # CI/CD pipeline

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/Mahad-007/Prompt-Optimizer-MCP-for-LLMs.git
cd Prompt-Optimizer-MCP-for-LLMs

2. Install Dependencies

pip install -r requirements.txt

3. Run Tests

python test_server.py

4. Start the Server

# For local development (STDIO mode)
python server.py

# For deployment (HTTP mode)
python start.py

🛠️ Installation

Prerequisites

• Python 3.11 or higher
• pip package manager

Install Dependencies

# Install from requirements.txt
pip install -r requirements.txt

⚙️ Configuration

For Cursor IDE

Create .cursor/mcp.json:

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "python",
      "args": ["server.py"],
      "env": {}
    }
  }
}

For Other MCP Clients

Configure your MCP client to use:

• Command: python server.py
• Transport: STDIO (default)

📖 Usage Examples

Using the MCP Server

Once configured, you can use the tools through any MCP client:

Optimize a Prompt

# Generate creative variants
variants = optimize_prompt(
    raw_prompt="Write a story about a cat",
    style="creative"
)
# Returns: [
#   "Craft a compelling story about a cat",
#   "Imagine you're an expert in this field. Write a story about a cat",
#   "Write a story about a cat. in a way that captivates and inspires"
# ]

# Generate precise variants
variants = optimize_prompt(
    raw_prompt="Please write a very detailed explanation about machine learning",
    style="precise"
)
# Returns: [
#   "Write a detailed explanation about machine learning",
#   "• Write a detailed explanation about machine learning",
#   "Write a detailed explanation about machine learning Be specific and concise."
# ]

Score a Prompt

score = score_prompt(
    raw_prompt="Please write a very detailed explanation about machine learning",
    improved_prompt="Write an explanation about machine learning"
)
# Returns: 0.85 (high score due to length reduction and clarity improvement)

HTTP API Usage

When deployed, the server also provides HTTP endpoints:

# Health check
curl http://localhost:8000/health

# Optimize prompt
curl -X POST http://localhost:8000/optimize \
  -H "Content-Type: application/json" \
  -d '{"raw_prompt": "Write about AI", "style": "creative"}'

# Score prompt
curl -X POST http://localhost:8000/score \
  -H "Content-Type: application/json" \
  -d '{"raw_prompt": "Write about AI", "improved_prompt": "Write about artificial intelligence"}'

Direct Python Usage

from tools.optimize import optimize_prompt, score_prompt

# Optimize a prompt
variants = optimize_prompt("Write about AI", "creative")
print(f"Optimized variants: {variants}")

# Score a prompt
score = score_prompt("Write about AI", "Write about artificial intelligence")
print(f"Score: {score}")

🧪 Testing

Run the comprehensive test suite:

# Run all tests
python test_server.py

# Run unit tests
python -m unittest tests.test_optimize -v

# Run specific test classes
python -m unittest tests.test_optimize.TestOptimizePrompt
python -m unittest tests.test_optimize.TestScorePrompt
python -m unittest tests.test_optimize.TestIntegration

🚀 Deployment

Automated Deployment

Use the deployment script:

python deploy.py

This will:

1. Run all tests
2. Install dependencies
3. Run linting checks
4. Build Docker image (if available)
5. Create deployment package

Manual Deployment

Deploy to Smithery

1. Install Smithery CLI:

   npm install -g @smithery/cli
   

2. Authenticate:

   smithery auth login
   

3. Deploy:

   # Windows
   .\deploy.bat
   
   # Linux/macOS
   chmod +x deploy.sh
   ./deploy.sh
   

Deploy with Docker

# Build the image
docker build -t prompt-optimizer-mcp:latest .

# Run the container
docker run -p 8000:8000 prompt-optimizer-mcp:latest

Deploy to Other Platforms

The server supports both STDIO (for MCP clients) and HTTP (for web deployment) transports:

• STDIO Mode: python server.py (for MCP clients)
• HTTP Mode: python start.py (for web deployment)

Your MCP server will be available at: https://prompt-optimizer-mcp.smithery.ai

For detailed deployment instructions, see DEPLOYMENT.md.

🔧 Development

Adding New Optimization Styles

1. Add the new style to the Literal type in server.py
2. Implement the style function in tools/optimize.py
3. Add corresponding tests in tests/test_optimize.py

Extending the Scoring Algorithm

Modify the score_prompt function in tools/optimize.py to include additional metrics or adjust weights.

Running Locally

# Start the MCP server (STDIO mode)
python server.py

# Start the HTTP server (deployment mode)
python http_server.py

# Auto-detect mode based on environment
python start.py

📊 Performance

• Response Time: < 100ms for most operations
• Memory Usage: ~50MB typical
• CPU Usage: Minimal (stateless operations)
• Scalability: Auto-scales from 1-5 replicas on Smithery

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Setup

# Clone your fork
git clone https://github.com/yourusername/Prompt-Optimizer-MCP-for-LLMs.git
cd Prompt-Optimizer-MCP-for-LLMs

# Install dependencies
pip install -r requirements.txt

# Run tests
python test_server.py

# Make your changes and test
python demo.py

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Model Context Protocol for the MCP specification
• MCP Python SDK for the server framework
• Smithery for deployment platform

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: DEPLOYMENT.md

⭐ Star History

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