# MCP Prompt Optimizer
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://modelcontextprotocol.io/)
> 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
```bash
# 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`
```json
{
"mcpServers": {
"prompt-optimizer": {
"command": "python3",
"args": ["/path/to/mcp-prompt-optimizer/prompt_optimizer.py"],
"env": {}
}
}
}
```
## 🎮 Usage
### Basic Commands
```plaintext
# 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
```plaintext
# 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
```bash
# 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
5. **advanced_optimize**: Applies research-backed strategies
6. **get_domain_template**: Returns professional domain templates
7. **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
```bash
# 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](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:
```bibtex
@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}
}
```
---
**Built with ❤️ for the AI community**
For questions, issues, or contributions, please visit our [GitHub repository](https://github.com/Bubobot-Team/mcp-prompt-optimizer).
