MCP-Troubleshooting-Server

MyMindVentures/MCP-Troubleshooting-Server

3.2

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The MCP Troubleshooting Gateway is a server designed to enhance AI troubleshooting by preventing repetitive errors and improving performance through learning and validation.

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🔧 MCP Troubleshooting Server

MCP Gateway for AI Troubleshooting - Central hub that prevents AI agents from repeating errors and provides comprehensive troubleshooting knowledge

Production Status NorthFlank MCP

🎯 Overview

The MCP Troubleshooting Server is the guardian angel of the Proof of Mind Ecosystem, preventing AI agents from repeating the same mistakes and providing intelligent guidance when problems arise. It serves as a comprehensive knowledge base for troubleshooting, error prevention, and AI agent education.

✨ Key Features

🛡️ Error Prevention System

  • Pattern Recognition: Identifies recurring error patterns
  • Proactive Guidance: Warns agents before problems occur
  • Learning Database: Stores solutions to common problems
  • Predictive Analysis: Anticipates potential issues

🧠 Intelligent Troubleshooting

  • Context-Aware Solutions: Provides relevant solutions based on context
  • Step-by-Step Guidance: Breaks down complex problems into manageable steps
  • Multi-Agent Support: Works with Devin, Cursor, and other AI builders
  • Real-time Assistance: Immediate help when agents encounter issues

📚 Knowledge Management

  • Solution Database: Comprehensive database of known solutions
  • Best Practices: Curated collection of development best practices
  • Error Documentation: Detailed documentation of common errors
  • Learning System: Continuously learns from new problems and solutions

🔄 MCP Integration

  • Gateway Functionality: Acts as MCP gateway for troubleshooting
  • Cross-Project Communication: Shares knowledge across all ecosystem projects
  • Real-time Updates: Live updates of troubleshooting knowledge
  • API Integration: Seamless integration with all MCP servers

🏗️ Architecture

graph TB
    A[MCP Troubleshooting Server] --> B[Error Detector]
    A --> C[Solution Engine]
    A --> D[Knowledge Base]
    A --> E[MCP Gateway]

    B --> F[Pattern Analyzer]
    B --> G[Context Extractor]
    B --> H[Risk Assessor]

    C --> I[Solution Matcher]
    C --> J[Step Generator]
    C --> K[Validation Engine]

    D --> L[Error Database]
    D --> M[Solution Library]
    D --> N[Best Practices]
    D --> O[Learning System]

    E --> P[MCP Router]
    E --> Q[Protocol Handler]
    E --> R[Response Formatter]

🚀 Quick Start

Prerequisites

  • Node.js 18+
  • Access to AI development agents
  • MCP server environment

Installation

# Clone the repository
git clone https://github.com/MyMindVentures/MCP-Troubleshooting-Server.git
cd MCP-Troubleshooting-Server

# Install dependencies
npm install

# Start development server
npm run dev

Production Deployment

# Deploy to NorthFlank (automatic CI/CD)
git push origin main
# NorthFlank will automatically build and deploy

📋 API Endpoints

Troubleshooting

  • POST /api/troubleshoot - Submit problem for analysis
  • GET /api/solutions/:id - Get solution by ID
  • POST /api/solutions - Add new solution
  • PUT /api/solutions/:id - Update existing solution
  • GET /api/solutions/search - Search solutions

Error Management

  • POST /api/errors - Report new error
  • GET /api/errors - List all errors
  • GET /api/errors/:id - Get error details
  • PUT /api/errors/:id/resolve - Mark error as resolved
  • GET /api/errors/patterns - Get error patterns

Knowledge Base

  • GET /api/knowledge - Get troubleshooting knowledge
  • POST /api/knowledge - Add new knowledge
  • PUT /api/knowledge/:id - Update knowledge
  • DELETE /api/knowledge/:id - Delete knowledge
  • GET /api/knowledge/search - Search knowledge base

MCP Gateway

  • POST /mcp/troubleshoot - MCP troubleshooting endpoint
  • GET /mcp/health - MCP health check
  • POST /mcp/learn - Learn from new problems
  • GET /mcp/status - MCP gateway status

🔧 Configuration

Environment Variables

# Core Configuration
NODE_ENV=production
PORT=3003
LOG_LEVEL=info

# Database
DATABASE_URL=sqlite://./data/troubleshooting.db

# AI Services
OPENAI_API_KEY=your_openai_key
ANTHROPIC_API_KEY=your_anthropic_key

# MCP Integration
MCP_SERVER_URL=http://localhost:3000
MCP_AUTH_TOKEN=your_mcp_token

# Knowledge Base
KNOWLEDGE_BASE_PATH=./data/knowledge
SOLUTION_DATABASE_PATH=./data/solutions

MCP Server Configuration

The troubleshooting server integrates with multiple MCP servers:

  • Doppler MCP: Secret management
  • NorthFlank MCP: Deployment troubleshooting
  • GitHub MCP: Version control issues
  • Memory MCP: Persistent troubleshooting knowledge

🧠 Troubleshooting Process

1. Problem Detection

Error Occurs → Pattern Analysis → Context Extraction → Risk Assessment

2. Solution Generation

Problem Analysis → Solution Matching → Step Generation → Validation

3. Knowledge Learning

New Problem → Analysis → Solution Creation → Knowledge Update

4. Prevention System

Pattern Recognition → Risk Prediction → Proactive Guidance → Prevention

🛠️ Development

Project Structure

src/
├── database/         # Database operations
├── knowledge-base/   # Knowledge management
├── models/          # Data models
├── services/        # External service integrations
└── index.js        # Application entry point

Adding New Troubleshooting Rules

  1. Create rule in src/knowledge-base/rules/
  2. Implement pattern matching logic
  3. Add solution generation
  4. Test with various scenarios
  5. Deploy to production

Knowledge Base Schema

// Error Pattern
const ErrorPattern = {
  id: 'uuid',
  pattern: 'regex|string',
  context: 'object',
  severity: 'low|medium|high|critical',
  frequency: 'number',
  lastSeen: 'timestamp',
};

// Solution
const Solution = {
  id: 'uuid',
  errorPatternId: 'uuid',
  title: 'string',
  description: 'string',
  steps: ['array'],
  prerequisites: ['array'],
  successRate: 'number',
  lastUpdated: 'timestamp',
};

📊 Analytics & Monitoring

Key Metrics

  • Error Resolution Rate: Percentage of errors successfully resolved
  • Solution Effectiveness: Success rate of provided solutions
  • Learning Speed: How quickly new problems are solved
  • Prevention Accuracy: Accuracy of proactive guidance

Real-time Monitoring

  • Active Problems: Currently unresolved issues
  • Solution Performance: Real-time solution effectiveness
  • Knowledge Growth: New knowledge being added
  • Agent Interactions: AI agent usage patterns

🔒 Security

Data Protection

  • Encrypted Storage: All troubleshooting data encrypted
  • Access Control: Role-based permissions
  • Audit Logging: Track all troubleshooting activities
  • Privacy Protection: Sensitive data anonymization

API Security

  • Authentication: JWT-based authentication
  • Rate Limiting: Prevent abuse of API endpoints
  • Input Validation: Sanitize all inputs
  • CORS Protection: Secure cross-origin requests

📈 Performance

Optimization Features

  • Intelligent Caching: Cache frequently accessed solutions
  • Pattern Preprocessing: Pre-process common patterns
  • Lazy Loading: Load knowledge on demand
  • Connection Pooling: Efficient database connections

Benchmarks

  • Response Time: < 100ms for solution queries
  • Throughput: 1000+ troubleshooting requests per second
  • Uptime: 99.9% availability target
  • Accuracy: 95%+ solution effectiveness

🧪 Testing

Test Strategy

# Unit tests
npm test

# Integration tests
npm run test:integration

# Troubleshooting tests
npm run test:troubleshooting

# Performance tests
npm run test:performance

Test Scenarios

  • Error Pattern Recognition: Test pattern matching accuracy
  • Solution Generation: Test solution quality
  • Knowledge Learning: Test learning from new problems
  • MCP Integration: Test MCP gateway functionality

📚 Documentation

Additional Resources

Support

  • GitHub Issues: Bug reports and feature requests
  • Discussions: Community discussions
  • Wiki: Additional documentation
  • Email: support@mymindventures.com

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

  • AI Development Community: For sharing troubleshooting knowledge
  • MCP Community: For Model Context Protocol
  • OpenAI: For GPT-4 integration
  • Anthropic: For Claude integration
  • Neurodiverse Community: For inspiration and feedback

Built with ❤️ for the AI development community

"Preventing problems before they happen, solving them when they do"