rsp2k/mcp-pdf

# 1️⃣ Clone and install
git clone https://github.com/rpm/mcp-pdf
cd mcp-pdf
uv sync

# 2️⃣ Install system dependencies (Ubuntu/Debian)
sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript

# 3️⃣ Verify installation
uv run python examples/verify_installation.py

# 4️⃣ Run the MCP server
uv run mcp-pdf

# Complete financial report analysis in seconds
health = await analyze_pdf_health("quarterly-report.pdf")
classification = await classify_content("quarterly-report.pdf") 
summary = await summarize_content("quarterly-report.pdf", summary_length="medium")
tables = await extract_tables("quarterly-report.pdf", pages=[5,6,7])
charts = await extract_charts("quarterly-report.pdf")

# Get instant insights
{
  "document_type": "Financial Report",
  "health_score": 9.2,
  "key_insights": [
    "Revenue increased 23% YoY",
    "Operating margin improved to 15.3%",
    "Strong cash flow generation"
  ],
  "tables_extracted": 12,
  "charts_found": 8,
  "processing_time": 2.1
}

# Comprehensive security analysis
security = await analyze_pdf_security("sensitive-document.pdf")
watermarks = await detect_watermarks("sensitive-document.pdf")
health = await analyze_pdf_health("sensitive-document.pdf")

# Enterprise-grade security insights
{
  "encryption_type": "AES-256",
  "permissions": {
    "print": false,
    "copy": false,
    "modify": false
  },
  "security_warnings": [],
  "watermarks_detected": true,
  "compliance_ready": true
}

# Advanced research paper analysis
layout = await analyze_layout("research-paper.pdf", pages=[1,2,3])
summary = await summarize_content("research-paper.pdf", summary_length="long")
citations = await extract_text("research-paper.pdf", pages=[15,16,17])

# Research intelligence delivered
{
  "reading_complexity": "Graduate Level",
  "main_topics": ["Machine Learning", "Natural Language Processing"],
  "citation_count": 127,
  "figures_detected": 15,
  "methodology_extracted": true
}

graph TD
    A[PDF Input] --> B{Smart Detection}
    B --> C{Document Type}
    C -->|Text-based| D[PyMuPDF Fast Path]
    C -->|Scanned| E[OCR Processing]
    C -->|Complex Layout| F[pdfplumber Analysis]
    C -->|Tables Heavy| G[Camelot + Tabula]
    
    D -->|Success| H[✅ Content Extracted]
    D -->|Fail| I[pdfplumber Fallback]
    I -->|Fail| J[pypdf Fallback]
    
    E --> K[Tesseract OCR]
    K --> L[AI Content Analysis]
    
    F --> M[Layout Intelligence]
    G --> N[Table Intelligence]
    
    H --> O[🧠 AI Enhancement]
    L --> O
    M --> O  
    N --> O
    
    O --> P[🎯 Structured Intelligence]

# Process PDFs directly from anywhere on the web
report_url = "https://company.com/annual-report.pdf"
analysis = await classify_content(report_url)  # Downloads & caches automatically
tables = await extract_tables(report_url)     # Uses cache - instant!
summary = await summarize_content(report_url) # Lightning fast!

# Enterprise-grade document assessment
health = await analyze_pdf_health("critical-document.pdf")

{
  "overall_health_score": 9.2,
  "corruption_detected": false,
  "optimization_potential": "23% size reduction possible",
  "security_assessment": "enterprise_ready",
  "recommendations": [
    "Document is production-ready",
    "Consider optimization for web delivery"
  ],
  "processing_confidence": 99.8
}

# Automatically understand document types
classification = await classify_content("mystery-document.pdf")

{
  "document_type": "Financial Report",
  "confidence": 97.3,
  "key_topics": ["Revenue", "Operating Expenses", "Cash Flow"],
  "complexity_level": "Professional",
  "suggested_tools": ["extract_tables", "extract_charts", "summarize_content"],
  "industry_vertical": "Technology"
}

# Process ALL document formats with unified intelligence
pdf_analysis = await pdf_tools.classify_content("report.pdf")
word_analysis = await office_tools.detect_office_format("report.docx")
excel_data = await office_tools.extract_text("data.xlsx")

# Cross-format document comparison
comparison = await compare_cross_format_documents([
    pdf_analysis, word_analysis, excel_data
])