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MCP Self-Learning Server

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

šŸ”„ 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

šŸ“Š 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

šŸ’¾ 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

šŸ“ 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

šŸš€ 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

šŸ“‹ 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

šŸ› ļø 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 }

šŸ“Š Monitoring & Analytics

Health Check Results

The health check tool verifies:

  • āœ… Server startup functionality

  • āœ… Data persistence system

  • āœ… Logging system

  • āœ… 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%

šŸ—‚ļø Directory Structure

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

šŸ”§ 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

  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

šŸ“ˆ 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

šŸ¤ 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

šŸ“ License

ISC License

šŸ†˜ 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


Built with ā¤ļø for autonomous learning and continuous improvement

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