ContentHarvester MCP System

A sophisticated content harvesting and pattern recognition system built as an MCP plugin, leveraging Cloudflare's edge infrastructure for distributed processing and analysis.

Core Capabilities

1. Pattern Recognition Engine

• Multi-layer Analysis: Implements recursive pattern recognition across multiple abstraction layers
• Evolution Tracking: Monitors pattern development and adaptation over time
• Optimization Engine: Continuously refines pattern identification and processing
• Contextual Understanding: Develops deep understanding of content relationships

2. Content Processing

• Distributed Harvesting: Efficient content collection at scale
• Pattern-based Analysis: Intelligent content categorization and relationship mapping
• Evolution Tracking: Monitors content development patterns
• Adaptive Processing: Self-optimizing processing pipelines

3. System Integration

• Cloudflare Infrastructure: Leverages edge computing capabilities
• MCP Plugin Architecture: Seamless integration with Model Context Protocol
• Distributed Storage: Efficient data management across D1, R2, and KV
• Commander Integration: Advanced command-line interface for system control

Quick Start

# Clone the repository
git clone https://github.com/devopsfranco/content-harvester-mcp.git
cd content-harvester-mcp

# Set up environment variables
export CF_API_TOKEN="your-cloudflare-api-token"
export CF_ACCOUNT_ID="your-cloudflare-account-id"

# Run development setup
./scripts/setup-dev.sh

Architecture Overview

Pattern Recognition Layer

interface Pattern {
  id: string;
  type: PatternType;
  confidence: number;
  metrics: PatternMetrics;
  evolution: EvolutionChain[];
}

Evolution Tracking

interface EvolutionChain {
  timestamp: number;
  state: PatternState;
  confidence: number;
  adaptations: Adaptation[];
}

System Integration

class ContentHarvester {
  private patterns: PatternRecognitionEngine;
  private evolution: EvolutionEngine;
  private storage: StorageManager;
}

Development

Environment Setup

1. Install dependencies:

npm install

2. Configure development environment:

mcp-configure \
  --api-token $CF_API_TOKEN \
  --account-id $CF_ACCOUNT_ID \
  --environment development

3. Deploy to development:

npm run dev

Testing

# Run test suite
npm test

# Run with pattern analysis
npm test -- --pattern-analysis

Deployment

Production Deployment

# Deploy to production
npm run deploy

# Configure MCP plugin
mcp-deploy \
  --name content-harvester \
  --worker-name content-harvester \
  --routes '*/*'

Monitoring

# View pattern evolution
mcp-monitor patterns

# Track system metrics
mcp-monitor metrics

Command Interface

Content Harvesting

# Basic content harvest
mcp harvest https://example.com

# Advanced pattern recognition
mcp harvest https://example.com \
  --depth 3 \
  --patterns "semantic,structural,evolutionary"

Pattern Analysis

# Analyze content patterns
mcp analyze <content-id>

# Track pattern evolution
mcp evolve <pattern-id>

System Components

1. Pattern Recognition

• Foundational pattern extraction
• Behavioral analysis
• Evolution tracking
• Adaptive optimization

2. Content Processing

• Multi-stage processing pipeline
• Pattern-based optimization
• Distributed execution
• Adaptive scaling

3. Storage Layer

• D1: Structured data storage
• R2: Raw content storage
• KV: Pattern caching
• Evolution tracking

Pattern Types

1. Structural Patterns

   • Content organization
   • Relationship mapping
   • Hierarchical structures
   • Component interactions

2. Behavioral Patterns

   • Content evolution
   • Usage patterns
   • Interaction flows
   • Adaptation trends

3. Evolution Patterns

   • Pattern development
   • Adaptation tracking
   • Optimization paths
   • Growth trajectories

Contributing

1. Fork the repository
2. Create your feature branch:

git checkout -b feature/amazing-feature

3. Implement your changes
4. Push to the branch:

git push origin feature/amazing-feature

5. Open a Pull Request

Best Practices

Pattern Recognition

• Start with broad pattern identification
• Progressively refine pattern matching
• Maintain pattern evolution history
• Validate pattern confidence

System Integration

• Use distributed processing
• Implement efficient caching
• Monitor system metrics
• Optimize resource usage

Development Flow

• Follow recursive optimization
• Maintain comprehensive testing
• Document pattern evolution
• Track system performance

License

MIT

Acknowledgments

• Cloudflare Workers platform
• Model Context Protocol
• Pattern recognition community
• Open source contributors