MCP Self-Learning Server

A sophisticated Model Context Protocol (MCP) server that autonomously learns from interactions, optimizes performance, and continuously improves its knowledge base through pattern recognition and machine learning techniques.

🌟 Features

🧠 Autonomous Learning Engine

• Pattern Recognition: Automatically identifies and learns from interaction patterns

• Feature Extraction: Analyzes tool sequences, context, performance metrics, and semantic embeddings

• Confidence Scoring: Evaluates pattern reliability based on frequency, recency, and consistency

• Memory Consolidation: Manages short-term and long-term pattern storage

🔄 Knowledge Synchronization

• Auto-sync: Every 60 seconds between MCP servers

• Knowledge Export/Import: JSON and Markdown formats

• Pattern Merging: With deduplication

• Cross-server Learning: Through shared knowledge directory

📊 Self-Improvement Capabilities

• Performance Optimization: Identifies redundancies and bottlenecks

• Predictive Suggestions: Anticipates next actions based on learned patterns

• Error Pattern Analysis: Learns from failures to improve success rates

• Adaptive Recommendations: Generates context-aware optimizations

💾 Data Persistence

• Automatic Data Saving: Every 5 minutes with backup rotation

• Learning Data Recovery: Loads previous sessions on startup

• Export Knowledge: Multiple formats (JSON, Markdown)

• Backup System: Automatic backup creation before saves

📝 Advanced Logging

• Multi-level Logging: Debug, Info, Warn, Error with colors and emojis

• File & Console Output: Simultaneous logging to both

• Log Rotation: Prevents disk space issues

• Performance Monitoring: Tool execution times and memory usage

Related MCP server: browser-mcp

🚀 Quick Start

Prerequisites

• Node.js 18+

• npm or yarn

Installation

1. Clone/Download the Project

   cd ~/saralegui-solutions-llc/shared/MCPSelfLearningServer

2. Install Dependencies

   npm install

3. Configure Claude Desktop

   Add to ~/.config/Claude/claude_desktop_config.json:

   {
     "mcpServers": {
       "self-learning": {
         "command": "node",
         "args": ["/home/ben/saralegui-solutions-llc/shared/MCPSelfLearningServer/mcp-self-learning-server.js"],
         "env": {
           "NODE_ENV": "production",
           "LEARNING_MODE": "autonomous"
         }
       }
     }
   }

4. Start the Server

   npm start

📋 Available Commands

Development & Testing

npm run dev          # Start in development mode
npm run debug        # Start with debug logging
npm test             # Run all tests
npm run test:unit    # Run unit tests only
npm run test:integration  # Run integration tests only

Monitoring & Health

npm run health       # Run comprehensive health check
npm run monitor      # Real-time monitoring
npm run monitor:details  # Detailed monitoring with change tracking

Manual Operations

# Health check
node tools/health-check.js

# Real-time monitoring
node tools/monitor.js [--interval 5] [--details]

# Start server directly
node mcp-self-learning-server.js

🛠️ Available MCP Tools

Core Learning Tools

analyze_pattern

Analyze and learn from interaction patterns

{
  "interaction": {
    "type": "tool_usage",
    "input": "user input",
    "output": "tool output", 
    "context": {},
    "performance": { "duration": 100 },
    "success": true
  }
}

get_insights

Get current learning analytics and insights

{}

trigger_learning

Manually trigger a learning cycle

{}

Knowledge Management

export_knowledge

Export learned knowledge to file

{
  "format": "json|markdown"  // Optional, defaults to json
}

import_knowledge

Import knowledge from external source

{
  "source": "file_path_or_url",
  "format": "json"  // Optional
}

Performance & Optimization

optimize_tool

Get optimization suggestions for specific tools

{
  "tool_name": "example_tool"  // Optional
}

predict_next_action

Get predictive suggestions based on current context

{
  "context": {
    "current_tool": "analyze_pattern",
    "user_intent": "optimization"
  }
}

get_performance_metrics

Get detailed performance analytics

{
  "tool_name": "specific_tool"  // Optional, for tool-specific metrics
}

📊 Monitoring & Analytics

Health Check Results

The health check tool verifies:

• ✅ Server startup functionality

• ✅ Data persistence system

• ✅ Logging system

• ✅ Performance metrics (startup time)

Real-time Monitoring

The monitor displays:

• Learning engine status (patterns, knowledge, cycles)

• Log file metrics and activity

• System resource usage

• Change indicators showing growth over time

Performance Expectations

🗂️ Directory Structure

MCPSelfLearningServer/
├── mcp-self-learning-server.js    # Main server file
├── package.json                   # Dependencies and scripts
├── README.md                      # This file
├── data/                         # Persistent learning data
│   ├── learning-engine.json      # Main learning data
│   └── learning-engine.backup.json # Backup
├── logs/                         # Server logs
│   └── mcp-server.log           # Main log file
├── lib/                         # Shared libraries
│   └── logger.js               # Enhanced logging system
├── test/                       # Test suites
│   ├── unit/                  # Unit tests
│   └── integration/          # Integration tests
└── tools/                    # Development tools
    ├── health-check.js      # Health check tool
    └── monitor.js          # Real-time monitoring

🔧 Configuration

Environment Variables

Learning Engine Settings

• Max Memory Size: 1000 patterns in memory

• Auto-save Interval: 5 minutes

• Pattern Confidence Threshold: 0.5

• Learning Trigger: Every 100 interactions or 50 tool uses

🚨 Troubleshooting

Common Issues

1. Server Won't Start

   • Check Node.js version (18+ required)

   • Verify all dependencies installed: npm install

   • Check file permissions

2. Data Not Persisting

   • Verify data/ directory permissions

   • Check disk space

   • Review logs for errors: tail -f logs/mcp-server.log

3. High Memory Usage

   • Run health check: npm run health

   • Check pattern count: npm run monitor

   • Consider reducing max memory size

4. Slow Performance

   • Enable performance logging: npm run debug

   • Check system resources

   • Review learning cycle frequency

Log Analysis

# View recent logs
tail -f logs/mcp-server.log

# Search for errors
grep "ERROR" logs/mcp-server.log

# Count log levels
grep -c "INFO\|WARN\|ERROR" logs/mcp-server.log

📈 Expected Learning Outcomes

Immediate (0-100 interactions)

• Basic pattern recognition active

• Initial knowledge base building

• Tool usage tracking enabled

Short-term (100-1000 interactions)

• Pattern confidence scores stabilizing

• First optimization recommendations

• Predictive accuracy ~50%

Long-term (1000+ interactions)

• Predictive accuracy >70%

• Response time improvements ~30%

• Comprehensive knowledge graph

• Cross-server knowledge sharing

• Self-documenting insights

🤝 Integration with Claude

Once configured, the server provides these tools in Claude:

• Pattern analysis for learning from conversations

• Performance insights for optimization

• Predictive suggestions for improved responses

• Knowledge export for documentation

• Real-time learning from every interaction

📝 License

ISC License

🆘 Support

For issues or questions:

1. Run health check: npm run health

2. Check logs: tail -f logs/mcp-server.log

3. Review this documentation

4. Check server status: npm run monitor

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