Prompt Auto-Optimizer MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Prompt Auto-Optimizer MCP start optimizing a prompt for summarizing research papers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Prompt Auto-Optimizer MCP
AI-Powered Prompt Evolution - An MCP server that automatically optimizes your AI prompts using evolutionary algorithms.
🎯 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.
Related MCP server: MCP Prompt Optimizer
🛠️ 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⚙️ 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"
}
}
}
}🔧 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
📝 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
🔧 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 iterationsAvailable Tools
12 toolsgepa_create_backupC
Create system backup including evolution state and trajectories
Inputs: label, includeTrajectories.
gepa_evaluate_promptC
Evaluate prompt candidate performance across multiple tasks
Inputs: promptId (required), taskIds (required), rolloutCount, parallel.
gepa_get_pareto_frontierC
Retrieve optimal candidates from Pareto frontier
Inputs: minPerformance, taskFilter, limit.
gepa_integrity_checkC
Perform comprehensive data integrity check
Inputs: component, autoRepair.
gepa_list_backupsB
List available system backups
Inputs: limit, filterLabel.
gepa_record_trajectoryC
Record execution trajectory for prompt evaluation
Inputs: promptId (required), taskId (required), executionSteps (required), result (required), +1 more.
gepa_recover_componentC
Recover a specific GEPA component
Inputs: componentType (required), strategy.
gepa_recovery_statusC
Get comprehensive disaster recovery status and health information
Inputs: includeMetrics.
gepa_reflectC
Analyze failures and generate prompt improvements
Inputs: trajectoryIds (required), targetPromptId (required), analysisDepth, focusAreas.
gepa_restore_backupC
Restore system from a specific backup
Inputs: backupId (required), validateIntegrity, createPreRestoreBackup.
gepa_select_optimalC
Select best prompt candidate for given context
Inputs: taskContext, performanceWeight, diversityWeight.
gepa_start_evolutionC
Initialize evolution process with configuration and seed prompt
Inputs: taskDescription (required), seedPrompt, targetModules, config.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 12 tools
Most tools have distinct purposes, but some overlap exists: gepa_evaluate_prompt and gepa_record_trajectory both relate to prompt evaluation, and gepa_select_optimal and gepa_get_pareto_frontier both involve selecting optimal candidates. The descriptions help clarify differences, but an agent might occasionally confuse these pairs.
All tool names follow a consistent 'gepa_verb_noun' pattern with snake_case, using clear verbs like create, evaluate, get, list, record, recover, restore, select, and start. This predictability makes it easy for agents to understand and use the toolset.
With 12 tools, the server is well-scoped for prompt auto-optimization, covering key areas like evaluation, evolution, backup, recovery, and selection. Each tool has a clear role, and the count is neither too sparse nor overwhelming for the domain.
The toolset provides comprehensive coverage for prompt optimization workflows, including evolution (start_evolution), evaluation (evaluate_prompt, record_trajectory), selection (select_optimal, get_pareto_frontier), reflection (reflect), and disaster recovery (backup, restore, integrity_check, recovery). A minor gap is the lack of tools for modifying or deleting components, but core operations are well-covered.
Maintenance
Related MCP Connectors
Cloudflare Workers MCP server: ai-prompt-optimizer
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server that lets your AI assistant build and deploy apps, hosting each one at a live URL.
MCP server for AI agent profiles and smart notes. 60+ coding prompt packs with expert personas.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceAn MCP server that evaluates prompts using AI to provide detailed feedback on clarity, completeness, and effectiveness.5ISC
- AlicenseNot gradedqualityDmaintenanceThis MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.25MIT
- AlicenseAqualityDmaintenanceAn MCP server that automatically enhances user prompts by applying advanced engineering techniques like chain-of-thought and few-shot reasoning based on identified intent. It optimizes technique selection through local learning and integrates directly into Claude sessions to improve output quality without additional API costs.6MIT
- AlicenseAqualityDmaintenanceAn advanced MCP server that intelligently enhances prompts using 44+ metaprompt strategies, with LLM-driven strategy selection and enterprise-grade features.853 npm9MIT