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

🚀 Quick Start

Prerequisites

Node.js 18+
npm or yarn

Installation

Clone/Download the Project
cd ~/saralegui-solutions-llc/shared/MCPSelfLearningServer
Install Dependencies
npm install
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" } } } }
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

Metric	Target	Excellent
Startup Time	<5s	<1s
Memory Usage	<100MB	<50MB
Response Time	<500ms	<100ms
Learning Accuracy	>70%	>90%

🗂️ 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

Variable	Default	Description
`NODE_ENV`	`production`	Environment mode
`LOG_LEVEL`	`info`	Logging level (debug/info/warn/error)
`LOG_CONSOLE`	`true`	Enable console logging
`LOG_FILE`	`true`	Enable file logging
`LEARNING_MODE`	`autonomous`	Learning behavior mode

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

Server Won't Start
- Check Node.js version (18+ required)
- Verify all dependencies installed: npm install
- Check file permissions
Data Not Persisting
- Verify data/ directory permissions
- Check disk space
- Review logs for errors: tail -f logs/mcp-server.log
High Memory Usage
- Run health check: npm run health
- Check pattern count: npm run monitor
- Consider reducing max memory size
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:

Run health check: npm run health
Check logs: tail -f logs/mcp-server.log
Review this documentation
Check server status: npm run monitor

Built with ❤️ for autonomous learning and continuous improvement

Deploy Server

HTTP connection URL

security – no known vulnerabilities

license - not tested

quality - confirmed to work

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Tools

Enables autonomous learning from interactions through pattern recognition and machine learning techniques. Continuously improves performance by analyzing tool usage, providing predictive suggestions, and sharing knowledge across MCP servers.

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