MCP Content Analyzer

MCP Content Analyzer

Enables comprehensive content analysis through web scraping, document processing (PDF, DOCX, TXT, RTF), screenshot analysis, and local Excel database management. Provides intelligent workflows for extracting, analyzing, and storing content from multiple sources with automated categorization and search capabilities.

Category
Visit Server

README

MCP Content Analyzer ✅ COMPLETE

A comprehensive MCP (Model Context Protocol) server system built with Hono and TypeScript that enables Claude to automatically scrape web content, process documents, analyze screenshots, and manage local Excel databases with intelligent content workflows.

🚀 ALL PHASES COMPLETE - Production Ready System ✅

Complete content analysis pipeline with web scraping, document processing, Excel database management, Docker deployment, and comprehensive documentation.

⚡ Quick Start (2 Minutes) - Easy Distribution

🚀 Recommended: Direct Installation (Bypasses npm cache issues)

# 1. Download and run the installation script
curl -fsSL https://raw.githubusercontent.com/DuncanDam/my-mcp/main/install.sh | bash

# 2. Setup dependencies and configuration (run once)
mcp-content-analyzer setup
mcp-content-analyzer config

# 3. Restart Claude Desktop completely

# 4. Start the analyzer
mcp-content-analyzer start

🔧 Alternative: Direct npm installation (may have cache issues)

# Note: Some users experience npm cache corruption with this method
npm install -g git+https://github.com/DuncanDam/my-mcp.git

# If the above fails, use the installation script method above instead

Traditional Setup:

  1. Automated Setup:

    ./scripts/setup.sh
    
  2. Restart Claude Desktop completely

  3. Test in Claude Desktop:

    Please test the MCP connection by calling test_connection with message "Hello MCP!"
    
  4. Try the main workflow:

    Use analyze_content_workflow to process https://example.com with topic "Testing"
    

🛠️ Complete Tool Suite

🌊 Main Workflow Tools (Recommended)

  • analyze_content_workflow - Complete content analysis pipeline with intelligent fallback
  • scrape_and_save_content - Web scraping workflow with Excel integration

🔧 System Tools

  • test_connection - Test MCP server connectivity
  • get_server_info - Get comprehensive server information

🕸️ Web Processing

  • scrape_webpage - Extract content from URLs with metadata
  • check_url_accessibility - Validate URLs before processing

📄 Document Processing

  • read_document - Extract content from PDF, DOCX, TXT, RTF files
  • analyze_document_metadata - Get document properties and structure
  • extract_document_text - Pure text extraction with formatting
  • process_extracted_text - Process Claude-extracted text from images

📊 Excel Database

  • add_content_entry - Add new entries to Excel database
  • search_similar_content - Find related existing content
  • get_topic_categories - Retrieve available topic categories
  • get_database_stats - Return database metrics and analytics

📚 Comprehensive Documentation

🚢 Deployment Options

Local Development

npm run build    # Build TypeScript
npm start       # Run MCP server
npm run dev     # Development mode with hot reload

Docker Deployment

./scripts/docker-deploy.sh    # Complete Docker setup
docker-compose up -d          # Manual Docker Compose

Production Scripts

./scripts/setup.sh           # Complete automated setup
./scripts/test-connection.sh # Comprehensive testing
./scripts/generate-config.sh # Claude Desktop configuration

🎯 Complete Workflow Examples

Example 1: Web Content Analysis

Use analyze_content_workflow to process https://techcrunch.com/ai-news with topic "AI News"

Example 2: Document Processing

Use analyze_content_workflow to process /path/to/research-paper.pdf with topic "Research"

Example 3: Screenshot Analysis

Share a screenshot with Claude, then:

Use analyze_content_workflow with the text you extracted from that screenshot, sourceDescription "Quarterly report slides", and topic "Business Reports"

🏗️ System Architecture

Complete 7-Phase Implementation:

  • Phase 1: Basic MCP server foundation
  • Phase 2: Excel database operations
  • Phase 3: Web scraping with Playwright
  • Phase 4: Document processing (PDF, DOCX, TXT, RTF)
  • Phase 5: Complete workflow & Hono integration
  • Phase 6: Docker & production setup
  • Phase 7: Documentation & testing suite

🔧 Technical Stack

  • Framework: Hono (ultra-fast web framework)
  • Runtime: Node.js 18+ with TypeScript
  • MCP SDK: @modelcontextprotocol/sdk
  • Web Scraping: Playwright + Cheerio
  • Document Processing: PDF.js, mammoth (DOCX), fs (TXT)
  • Database: ExcelJS for local Excel file management
  • Validation: Zod schemas with type safety
  • Containerization: Docker + Docker Compose
  • Vision Processing: Claude's native capabilities (no external APIs needed)

🛡️ Security & Production Features

  • Multi-stage Docker builds with security best practices
  • File validation and path traversal protection
  • Resource limits and health checks
  • Comprehensive error handling and logging
  • Type-safe operations throughout
  • No external API dependencies for core functionality

📊 Performance & Monitoring

  • Tool response time: < 5 seconds for web scraping
  • Document processing: < 3 seconds for small files, < 10 seconds for large files
  • Excel operations: < 1 second for database queries
  • Memory usage: < 512MB base, < 1GB with browsers
  • Health check endpoints and comprehensive logging

🆘 Support & Troubleshooting

  • Quick Test: Run ./scripts/test-connection.sh
  • Logs: tail -f ~/Library/Logs/Claude/mcp-server-content-analyzer.log
  • Health Check: curl http://localhost:3000/health (if using Hono)
  • Documentation: See COMPLETE-GUIDE.md for comprehensive testing

✅ Success Criteria

Your system is working correctly if:

  • ✅ All system tools respond (test_connection, get_server_info)
  • ✅ Web scraping works with real URLs
  • ✅ Document processing handles PDF, DOCX, TXT files
  • ✅ Claude vision integration processes screenshots
  • ✅ Excel database saves and retrieves content
  • ✅ Complete workflows execute end-to-end

Ready for production use! 🚀

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured