Which integrations are available for this server?

Supports knowledge export in Markdown format for human-readable documentation of learned patterns, insights, and performance analytics

How do I use MCP Self-Learning 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., "@MCP Self-Learning Server analyze my recent interactions and suggest optimizations" 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.

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

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

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