Prompt Auto Optimizer MCP

🚀 Prompt Auto-Optimizer MCP

AI-Powered Prompt Evolution - An MCP server that automatically optimizes your AI prompts using evolutionary algorithms.

🚀 Quick Start

The Prompt Auto - Optimizer MCP aims to automatically evolve and optimize AI prompts to enhance performance, creativity, and reliability. It leverages genetic algorithms to iteratively improve prompts based on real - world performance data.

📦 Installation

# Clone and install
git clone https://github.com/your-org/prompt-auto-optimizer-mcp.git
cd prompt-auto-optimizer-mcp
npm install
npm run build

# Start the MCP server
npm run mcp:start

📚 Documentation

⚙️ Configuration

Add the following to your Claude Code settings (.claude/settings.json):

{
  "mcp": {
    "servers": {
      "prompt-optimizer": {
        "command": "npx",
        "args": ["prompt-auto-optimizer-mcp"],
        "cwd": "./path/to/prompt-auto-optimizer-mcp"
      }
    }
  }
}

🔧 Available Tools

Core Optimization Tools

gepa_start_evolution

Initiate the optimization of a prompt using evolutionary algorithms.

{
  taskDescription: string;           // What you want to optimize for
  seedPrompt?: string;              // Starting prompt (optional)
  config?: {
    populationSize?: number;        // How many variants to test (default: 20)
    generations?: number;           // How many iterations (default: 10)
    mutationRate?: number;         // How much to change prompts (default: 0.15)
  };
}

gepa_evaluate_prompt

Test the performance of a prompt on specific tasks.

{
  promptId: string;                 // Which prompt to test
  taskIds: string[];               // What tasks to test it on
  rolloutCount?: number;           // How many times to test (default: 5)
}

gepa_reflect

Analyze the reasons for prompt failures and obtain improvement suggestions.

{
  trajectoryIds: string[];         // Which test runs to analyze
  targetPromptId: string;          // Which prompt needs improvement
  analysisDepth?: 'shallow' | 'deep'; // How detailed (default: 'deep')
}

gepa_get_pareto_frontier

Retrieve the best prompt candidates that balance multiple goals.

{
  minPerformance?: number;         // Minimum quality threshold
  limit?: number;                  // Max results to return (default: 10)
}

gepa_select_optimal

Select the best prompt for your specific use case.

{
  taskContext?: string;            // Describe your use case
  performanceWeight?: number;      // How much to prioritize accuracy (default: 0.7)
  diversityWeight?: number;        // How much to prioritize creativity (default: 0.3)
}

gepa_record_trajectory

Log the results of prompt executions for analysis.

{
  promptId: string;                // Which prompt was used
  taskId: string;                  // What task was performed
  executionSteps: ExecutionStep[]; // What happened during execution
  result: {
    success: boolean;              // Did it work?
    score: number;                 // How well did it work?
  };
}

Backup & Recovery Tools

• gepa_create_backup - Save the current optimization state
• gepa_restore_backup - Restore from a previous backup
• gepa_list_backups - Show available backups
• gepa_recovery_status - Check system health
• gepa_integrity_check - Verify data integrity

💻 Usage Examples

Basic Usage

1. Start Evolution: Use gepa_start_evolution with your task description.
2. Record Results: Use gepa_record_trajectory to log how prompts perform.
3. Analyze Failures: Use gepa_reflect to understand what went wrong.
4. Get Best Prompts: Use gepa_select_optimal to find the best candidates.

Environment Variables

# Optional performance tuning
GEPA_MAX_CONCURRENT_PROCESSES=3           # Parallel execution limit
GEPA_DEFAULT_POPULATION_SIZE=20            # Default prompt variants
GEPA_DEFAULT_GENERATIONS=10               # Default iterations

Built for better AI prompts • 📚 Docs • 🐛 Issues