MCP Prompt Optimizer

A professional-grade MCP (Model Context Protocol) server that provides cutting-edge prompt optimization tools with research-backed strategies delivering 15-74% performance improvements.

✨ Features

🎯 Basic Optimization Strategies

• Clarity: Simplifies prompts for directness and precision
• Specificity: Adds detailed constraints and requirements
• Chain of Thought: Incorporates step-by-step reasoning
• Few-Shot: Includes example formats for guidance
• Structured Output: Defines clear output organization
• Role-Based: Adds expert role context

🚀 Advanced Optimization Strategies

• Tree of Thoughts (ToT): Multi-path reasoning with 74% success rate on complex tasks
• Constitutional AI: Self-critique and alignment with safety principles
• Automatic Prompt Engineer (APE): AI-discovered optimal instruction patterns
• Meta-Prompting: AI generates its own optimized prompts
• Self-Refine: Iterative improvement with 20% performance gains
• TEXTGRAD: Natural language feedback as optimization gradients
• Medprompt: Multi-technique ensemble achieving 90%+ accuracy
• PromptWizard: Feedback-driven self-evolving prompts

📋 Professional Domain Templates

Production-ready templates across 11 domains:

• Business Analysis: Competitive analysis frameworks
• Product Management: User research synthesis
• Content Creation: Technical blog posts with SEO optimization
• Development: Comprehensive code review checklists
• Communication: Stakeholder updates and project reports
• Strategy: OKR planning frameworks
• Operations: Standard Operating Procedures (SOPs)
• Legal: Contract termination and compliance
• Customer Experience: Feedback surveys and insights
• Data Analysis: Data insights and reporting
• Meeting Management: Effective meeting agendas

🛠️ Installation

Quick Setup

# Clone the repository
git clone <repository-url>
cd mcp-prompt-optimizer

# Create virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
./install.sh

# Or install manually
pip install -r requirements.txt

# Configure Claude Desktop
python3 setup_interactive.py

Manual Configuration

Add to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "python3",
      "args": ["/path/to/mcp-prompt-optimizer/prompt_optimizer.py"],
      "env": {}
    }
  }
}

🎮 Usage

Basic Commands

# Analyze prompt quality
"Analyze this prompt: write a blog post about AI"

# Apply specific optimization
"Optimize this prompt using chain_of_thought: explain machine learning"

# Auto-select best strategy
"Auto-optimize: help me debug this code"

# Get domain template
"Get domain template for code_review_checklist"

Advanced Commands

# Use Tree of Thoughts for complex problems
"Apply advanced optimization with tree_of_thoughts: design a microservices architecture"

# Use Constitutional AI for safety-critical tasks
"Apply advanced optimization with constitutional_ai: create content moderation guidelines"

# Use Medprompt for high-accuracy classification
"Apply advanced optimization with medprompt: categorize customer support tickets"

# List available templates
"List all domain templates"

🏗️ Architecture

mcp-prompt-optimizer/
├── prompt_optimizer.py      # Main MCP server
├── advanced_strategies.py   # Research-backed optimization strategies
├── domain_templates.py      # Professional domain templates
├── examples.py              # Usage examples and demonstrations
├── setup_interactive.py     # Automated setup script
└── README.md               # This file

🧪 Testing

# Run basic tests
./test.sh

# Run usage examples
python3 examples.py

📊 Performance Benchmarks

Strategy	Use Case	Performance Improvement
Tree of Thoughts	Complex reasoning	70-74% success rate
Medprompt	Classification tasks	90%+ accuracy
Self-Refine	Iterative improvement	20% per iteration
Constitutional AI	Safety alignment	High compliance
Chain of Thought	Step-by-step tasks	15-25% improvement

🔧 Available Tools

Core Tools

1. analyze_prompt: Analyzes prompt quality and identifies issues
2. optimize_prompt: Applies specific optimization strategies
3. auto_optimize: Automatically selects optimal strategy
4. get_prompt_template: Returns basic templates

Advanced Tools

1. advanced_optimize: Applies research-backed strategies
2. get_domain_template: Returns professional domain templates
3. list_domain_templates: Lists available templates by domain

🎯 Strategy Selection Guide

Prompt Type	Recommended Strategy
Complex problems	tree_of_thoughts
Classification tasks	medprompt
Safety-critical	constitutional_ai
Vague requirements	meta_prompting
Needs refinement	self_refine
General optimization	auto

🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Update documentation
5. Submit a pull request

Adding New Features

• New Strategy: Add to advanced_strategies.py
• New Template: Add to domain_templates.py
• Examples: Add to examples.py

🐛 Troubleshooting

Common Issues

MCP not working?

• Check Python version: python3 --version (requires 3.8+)
• Install dependencies: Run ./install.sh or pip install -r requirements.txt
• Verify MCP installation: pip show mcp
• Check Claude Desktop logs
• Restart Claude Desktop

Commands not recognized?

• Verify configuration file location
• Check file paths in configuration
• Run setup script again

Debug Mode

# Test server directly
python3 prompt_optimizer.py

# Verbose logging
export MCP_LOG_LEVEL=debug
python3 prompt_optimizer.py

📄 License

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

🙏 Acknowledgments

• Research from Princeton, Google DeepMind, Microsoft Research
• Anthropic's Constitutional AI framework
• Stanford's DSPy framework
• OpenAI's prompt engineering guidelines

📈 Citation

If you use this tool in your research or projects, please cite:

@software{mcp_prompt_optimizer,
  title={MCP Prompt Optimizer: Research-Backed Prompt Optimization for AI Systems},
  author={Bubobot},
  year={2024},
  url={https://github.com/Bubobot-Team/mcp-prompt-optimizer}
}

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For questions, issues, or contributions, please visit our GitHub repository.