Which integrations are available for this server?

Utilizes OpenAI's API to generate vector embeddings for prompts, powering RAG-based retrieval and performance-history learning.

How do I use Prompt Learning MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Prompt Learning MCP Server optimize my prompt for summarizing research papers using past performance" 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 Learning MCP Server

Stateful prompt optimization that learns over time.

An MCP (Model Context Protocol) server that optimizes your prompts using research-backed techniques (APE, OPRO, DSPy patterns) and learns from performance history via embedding-based retrieval.

Features

🧠 Smart Optimization: Uses actual LLM-based evaluation, not heuristics
📚 Learns Over Time: Stores prompt performance in vector database
🔍 RAG-Powered: Retrieves similar high-performing prompts
⚡ Pattern-Based Quick Wins: Instant improvements without API calls
📊 Analytics: Track what's working across domains

Quick Install

curl -fsSL https://someclaudeskills.com/install/prompt-learning.sh | bash

Or manually:

cd ~/mcp-servers/prompt-learning npm install npm run build npm run setup

Requirements

Node.js 18+
Docker (for Qdrant and Redis)
OpenAI API key (for embeddings)

Usage

Once installed, use these tools in Claude Code:

`optimize_prompt`

Optimize a prompt using pattern-based and RAG-based techniques:

"optimize this prompt: summarize the document"

Returns the optimized prompt with improvement details.

`retrieve_prompts`

Find similar high-performing prompts:

"find similar prompts for: code review feedback"

`record_feedback`

Record how a prompt performed (enables learning):

"record that my last prompt succeeded with quality score 0.9"

`suggest_improvements`

Get quick suggestions without full optimization:

"suggest improvements for this prompt: [your prompt]"

`get_analytics`

View performance trends:

"show prompt analytics for the last 30 days"

How It Works

Cold Start (No History)

Pattern-based improvements: Adds structure, chain-of-thought, constraints
OPRO-style iteration: LLM generates candidates, evaluates, selects best
APE-style generation: Creates multiple instruction variants

Warm Start (With History)

Embed the prompt: Creates vector representation
Retrieve similar: Finds high-performing prompts from database
Learn from winners: Synthesizes improvements from what worked
Iterate with feedback: Uses evaluation to guide optimization

Evaluation

All prompts are scored by an LLM evaluator on:

Clarity (25%): How unambiguous
Specificity (25%): Appropriate guidance level
Completeness (20%): Covers all requirements
Structure (15%): Well-organized
Effectiveness (15%): Likely to produce desired output

Architecture

Claude Code │ │ MCP Protocol ▼ ┌─────────────────────────────┐ │ prompt-learning MCP Server │ │ │ │ Tools: │ │ • optimize_prompt │ │ • retrieve_prompts │ │ • record_feedback │ │ • suggest_improvements │ │ • get_analytics │ └─────────┬───────────────────┘ │ ┌─────┴─────┐ ▼ ▼ ┌───────┐ ┌───────┐ │Qdrant │ │ Redis │ │(Vector│ │(Cache)│ │ DB) │ │ │ └───────┘ └───────┘

Configuration

Claude Code Config (~/.claude.json)

{ "mcpServers": { "prompt-learning": { "command": "node", "args": ["~/mcp-servers/prompt-learning/dist/index.js"], "env": { "VECTOR_DB_URL": "http://localhost:6333", "REDIS_URL": "redis://localhost:6379", "OPENAI_API_KEY": "sk-..." } } } }

Environment Variables

Variable	Default	Description
`VECTOR_DB_URL`	`http://localhost:6333`	Qdrant server URL
`REDIS_URL`	`redis://localhost:6379`	Redis server URL
`OPENAI_API_KEY`	(required)	For embeddings

Development

# Install dependencies npm install # Run in development mode npm run dev # Build for production npm run build # Run setup (starts Docker, initializes DB) npm run setup # Run tests npm test

Troubleshooting

MCP Server Not Starting

Check Docker containers are running:

docker ps | grep prompt-learning

Vector DB Connection Failed

# Check Qdrant health curl http://localhost:6333/health # Restart Qdrant docker restart prompt-learning-qdrant

No Improvements Seen

Ensure OPENAI_API_KEY is set correctly
Check Claude Code logs: ~/.claude/logs/mcp.log
Try with a simple prompt first

Research Foundation

This server implements techniques from:

APE (Zhou et al., 2022): Automatic Prompt Engineer
OPRO (Yang et al., 2023): Optimization by Prompting
DSPy (Khattab et al., 2023): Programmatic prompt optimization
Contextual Retrieval (Anthropic, 2024): Enhanced embedding retrieval

License

MIT

Prompt Learning MCP Server

Prompt Learning MCP Server

Features

Quick Install

Requirements

Usage

`optimize_prompt`

`retrieve_prompts`

`record_feedback`

`suggest_improvements`

`get_analytics`

How It Works

Cold Start (No History)

Warm Start (With History)

Evaluation

Architecture

Configuration

Claude Code Config (~/.claude.json)

Environment Variables

Development

Troubleshooting

MCP Server Not Starting

Vector DB Connection Failed

No Improvements Seen

Research Foundation

License

Links

Resources

New MCP Servers

Latest Blog Posts

MCP directory API