-
securityA
license-
qualityAn MCP server that analyzes codebases and generates contextual prompts, making it easier for AI assistants to understand and work with code repositories.
Last updated -
14
MIT License
Prompt Auto-Optimizer MCP
AI-Powered Prompt Evolution - An MCP server that automatically optimizes your AI prompts using evolutionary algorithms.
đŻ Purpose
Automatically evolve and optimize AI prompts to improve performance, creativity, and reliability. Uses genetic algorithms to iteratively improve prompts based on real performance data.
đ ď¸ 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
âď¸ Configuration
Add 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"
}
}
}
}
đ§ Available Tools
Core Optimization Tools
gepa_start_evolution
Start optimizing 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 how well a prompt performs 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 why prompts fail and get 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
Get the best prompt candidates that balance multiple goals.
{
minPerformance?: number; // Minimum quality threshold
limit?: number; // Max results to return (default: 10)
}
gepa_select_optimal
Choose 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 current optimization stategepa_restore_backup- Restore from a previous backupgepa_list_backups- Show available backupsgepa_recovery_status- Check system healthgepa_integrity_check- Verify data integrity
đ Basic Usage
Start Evolution: Use
gepa_start_evolutionwith your task descriptionRecord Results: Use
gepa_record_trajectoryto log how prompts performAnalyze Failures: Use
gepa_reflectto understand what went wrongGet Best Prompts: Use
gepa_select_optimalto find the best candidates
đ§ 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 ⢠đ Docs ⢠đ Issues
Tools
An MCP server that automatically optimizes AI prompts using evolutionary algorithms, helping improve prompt performance, creativity, and reliability through iterative testing and refinement.
- đŻ Purpose
- đ ď¸ Installation
- âď¸ Configuration
- đ§ Available Tools
- đ Basic Usage
- đ§ Environment Variables
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