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README.md
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<img src="https://img.shields.io/badge/MCP-PDF%20Tools-red?style=for-the-badge&logo=adobe-acrobat-reader" alt="MCP PDF">
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**🚀 The Ultimate PDF Processing Intelligence Platform for AI**
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**A FastMCP server for PDF processing**
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*Transform any PDF into structured, actionable intelligence with 24 specialized tools*
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*41 tools for text extraction, OCR, tables, forms, annotations, and more*
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[](https://www.python.org/downloads/)
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[](https://github.com/jlowin/fastmcp)
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[](https://opensource.org/licenses/MIT)
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[](https://github.com/rsp2k/mcp-pdf)
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[](https://modelcontextprotocol.io)
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[](https://pypi.org/project/mcp-pdf/)
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**🤝 Perfect Companion to [MCP Office Tools](https://git.supported.systems/MCP/mcp-office-tools)**
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**Works great with [MCP Office Tools](https://git.supported.systems/MCP/mcp-office-tools)**
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</div>
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---
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## ✨ **What Makes MCP PDF Revolutionary?**
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## What It Does
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> 🎯 **The Problem**: PDFs contain incredible intelligence, but extracting it reliably is complex, slow, and often fails.
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>
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> ⚡ **The Solution**: MCP PDF delivers **AI-powered document intelligence** with **40 specialized tools** that understand both content and structure.
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MCP PDF extracts content from PDFs using multiple libraries with automatic fallbacks. If one method fails, it tries another.
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<table>
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<tr>
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<td>
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### 🏆 **Why MCP PDF Leads**
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- **🚀 40 Specialized Tools** for every PDF scenario
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- **🧠 AI-Powered Intelligence** beyond basic extraction
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- **🔄 Multi-Library Fallbacks** for 99.9% reliability
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- **⚡ 10x Faster** than traditional solutions
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- **🌐 URL Processing** with smart caching
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- **🎯 Smart Token Management** prevents MCP overflow errors
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</td>
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<td>
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### 📊 **Enterprise-Proven For:**
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- **Business Intelligence** & financial analysis
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- **Document Security** assessment & compliance
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- **Academic Research** & content analysis
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- **Automated Workflows** & form processing
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- **Document Migration** & modernization
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- **Content Management** & archival
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</td>
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</tr>
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</table>
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**Core capabilities:**
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- **Text extraction** via PyMuPDF, pdfplumber, or pypdf (auto-fallback)
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- **Table extraction** via Camelot, pdfplumber, or Tabula (auto-fallback)
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- **OCR** for scanned documents via Tesseract
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- **Form handling** - extract, fill, and create PDF forms
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- **Document assembly** - merge, split, reorder pages
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- **Annotations** - sticky notes, highlights, stamps
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- **Vector graphics** - extract to SVG for schematics and technical drawings
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---
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## 🚀 **Get Intelligence in 60 Seconds**
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## Quick Start
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```bash
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# Install from PyPI
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uvx mcp-pdf
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# Or add to Claude Code
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claude mcp add pdf-tools uvx mcp-pdf
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```
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<details>
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<summary><b>Development Installation</b></summary>
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```bash
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# 1️⃣ Clone and install
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git clone https://github.com/rsp2k/mcp-pdf
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cd mcp-pdf
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uv sync
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# 2️⃣ Install system dependencies (Ubuntu/Debian)
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# System dependencies (Ubuntu/Debian)
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sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript
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# 3️⃣ Verify installation
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uv run python examples/verify_installation.py
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# 4️⃣ Run the MCP server
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uv run mcp-pdf
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```
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<details>
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<summary>🔧 <b>Claude Desktop Integration</b> (click to expand)</summary>
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### **📦 Production Installation (PyPI)**
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```bash
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# For personal use across all projects
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claude mcp add -s local pdf-tools uvx mcp-pdf
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# For project-specific use (isolated)
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claude mcp add -s project pdf-tools uvx mcp-pdf
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```
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### **🛠️ Development Installation (Source)**
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```bash
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# For local development from source
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claude mcp add -s project pdf-tools-dev uv -- --directory /path/to/mcp-pdf run mcp-pdf
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```
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### **⚙️ Manual Configuration**
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Add to your `claude_desktop_config.json`:
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```json
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{
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"mcpServers": {
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"pdf-tools": {
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"command": "uvx",
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"args": ["mcp-pdf"]
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}
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}
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}
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```
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*Restart Claude Desktop and unlock PDF intelligence!*
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</details>
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---
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## 🎭 **See AI-Powered Intelligence In Action**
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### **📊 Business Intelligence Workflow**
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```python
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# Complete financial report analysis in seconds
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health = await analyze_pdf_health("quarterly-report.pdf")
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classification = await classify_content("quarterly-report.pdf")
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summary = await summarize_content("quarterly-report.pdf", summary_length="medium")
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# Smart table extraction - prevents token overflow on large tables
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tables = await extract_tables("quarterly-report.pdf", pages="5-7", max_rows_per_table=100)
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# Or get just table structure without data
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table_summary = await extract_tables("quarterly-report.pdf", pages="5-7", summary_only=True)
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charts = await extract_charts("quarterly-report.pdf")
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# Get instant insights
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{
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"document_type": "Financial Report",
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"health_score": 9.2,
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"key_insights": [
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"Revenue increased 23% YoY",
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"Operating margin improved to 15.3%",
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"Strong cash flow generation"
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],
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"tables_extracted": 12,
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"charts_found": 8,
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"processing_time": 2.1
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}
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```
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### **🔒 Document Security Assessment**
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```python
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# Comprehensive security analysis
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security = await analyze_pdf_security("sensitive-document.pdf")
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watermarks = await detect_watermarks("sensitive-document.pdf")
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health = await analyze_pdf_health("sensitive-document.pdf")
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# Enterprise-grade security insights
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{
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"encryption_type": "AES-256",
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"permissions": {
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"print": false,
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"copy": false,
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"modify": false
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},
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"security_warnings": [],
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"watermarks_detected": true,
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"compliance_ready": true
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}
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```
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### **📚 Academic Research Processing**
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```python
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# Advanced research paper analysis
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layout = await analyze_layout("research-paper.pdf", pages=[1,2,3])
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summary = await summarize_content("research-paper.pdf", summary_length="long")
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citations = await extract_text("research-paper.pdf", pages=[15,16,17])
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# Research intelligence delivered
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{
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"reading_complexity": "Graduate Level",
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"main_topics": ["Machine Learning", "Natural Language Processing"],
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"citation_count": 127,
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"figures_detected": 15,
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"methodology_extracted": true
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}
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```
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---
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## 🛠️ **Complete Arsenal: 40+ Specialized Tools**
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<div align="center">
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### **🎯 Document Intelligence & Analysis**
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| 🧠 **Tool** | 📋 **Purpose** | ⚡ **AI Powered** | 🎯 **Accuracy** |
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|-------------|---------------|-----------------|----------------|
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| `classify_content` | AI-powered document type detection | ✅ Yes | 97% |
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| `summarize_content` | Intelligent key insights extraction | ✅ Yes | 95% |
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| `analyze_pdf_health` | Comprehensive quality assessment | ✅ Yes | 99% |
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| `analyze_pdf_security` | Security & vulnerability analysis | ✅ Yes | 99% |
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| `compare_pdfs` | Advanced document comparison | ✅ Yes | 96% |
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### **📊 Core Content Extraction**
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| 🔧 **Tool** | 📋 **Purpose** | ⚡ **Speed** | 🎯 **Accuracy** |
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|-------------|---------------|-------------|----------------|
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| `extract_text` | Multi-method text extraction with auto-chunking | **Ultra Fast** | 99.9% |
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| `extract_tables` | Smart table extraction with token overflow protection | **Fast** | 98% |
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| `ocr_pdf` | Advanced OCR for scanned docs | **Moderate** | 95% |
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| `extract_images` | Media extraction & processing | **Fast** | 99% |
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| `pdf_to_markdown` | Structure-preserving conversion | **Fast** | 97% |
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### **📐 Visual & Layout Analysis**
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| 🎨 **Tool** | 📋 **Purpose** | 🔍 **Precision** | 💪 **Features** |
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|-------------|---------------|-----------------|----------------|
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| `analyze_layout` | Page structure & column detection | **High** | Advanced |
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| `extract_charts` | Visual element extraction | **High** | Smart |
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| `detect_watermarks` | Watermark identification | **Perfect** | Complete |
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</div>
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---
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## 🌟 **Document Format Intelligence Matrix**
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<div align="center">
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### **📄 Universal PDF Processing Capabilities**
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| 📋 **Document Type** | 🔍 **Detection** | 📊 **Text** | 📈 **Tables** | 🖼️ **Images** | 🧠 **Intelligence** |
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|---------------------|-----------------|------------|--------------|--------------|-------------------|
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| **Financial Reports** | ✅ Perfect | ✅ Perfect | ✅ Perfect | ✅ Perfect | 🧠 **AI-Enhanced** |
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| **Research Papers** | ✅ Perfect | ✅ Perfect | ✅ Excellent | ✅ Perfect | 🧠 **AI-Enhanced** |
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| **Legal Documents** | ✅ Perfect | ✅ Perfect | ✅ Good | ✅ Perfect | 🧠 **AI-Enhanced** |
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| **Scanned PDFs** | ✅ Auto-Detect | ✅ OCR | ✅ OCR | ✅ Perfect | 🧠 **AI-Enhanced** |
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| **Forms & Applications** | ✅ Perfect | ✅ Perfect | ✅ Excellent | ✅ Perfect | 🧠 **AI-Enhanced** |
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| **Technical Manuals** | ✅ Perfect | ✅ Perfect | ✅ Perfect | ✅ Perfect | 🧠 **AI-Enhanced** |
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*✅ Perfect • 🧠 AI-Enhanced Intelligence • 🔍 Auto-Detection*
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</div>
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|
||||
---
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## ⚡ **Performance That Amazes**
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<div align="center">
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### **🚀 Real-World Benchmarks**
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| 📄 **Document Type** | 📏 **Pages** | ⏱️ **Processing Time** | 🆚 **vs Competitors** | 🧠 **Intelligence Level** |
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|---------------------|-------------|----------------------|----------------------|---------------------------|
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| Financial Report | 50 pages | 2.1 seconds | **10x faster** | **AI-Powered** |
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| Research Paper | 25 pages | 1.3 seconds | **8x faster** | **Deep Analysis** |
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| Scanned Document | 100 pages | 45 seconds | **5x faster** | **OCR + AI** |
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| Complex Forms | 15 pages | 0.8 seconds | **12x faster** | **Structure Aware** |
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*Benchmarked on: MacBook Pro M2, 16GB RAM • Including AI processing time*
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|
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</div>
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|
||||
---
|
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## 🏗️ **Intelligent Architecture**
|
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|
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### **🧠 Multi-Library Intelligence System**
|
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*Never worry about PDF compatibility or failure again*
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|
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```mermaid
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graph TD
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A[PDF Input] --> B{Smart Detection}
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B --> C{Document Type}
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C -->|Text-based| D[PyMuPDF Fast Path]
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C -->|Scanned| E[OCR Processing]
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C -->|Complex Layout| F[pdfplumber Analysis]
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C -->|Tables Heavy| G[Camelot + Tabula]
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D -->|Success| H[✅ Content Extracted]
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D -->|Fail| I[pdfplumber Fallback]
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I -->|Fail| J[pypdf Fallback]
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E --> K[Tesseract OCR]
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K --> L[AI Content Analysis]
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|
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F --> M[Layout Intelligence]
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G --> N[Table Intelligence]
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|
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H --> O[🧠 AI Enhancement]
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L --> O
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M --> O
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N --> O
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O --> P[🎯 Structured Intelligence]
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```
|
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|
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### **🎯 Intelligent Processing Pipeline**
|
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|
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1. **🔍 Smart Detection**: Automatically identify document type and optimal processing strategy
|
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2. **⚡ Optimized Extraction**: Use the fastest, most accurate method for each document
|
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3. **🛡️ Fallback Protection**: Seamless method switching if primary approach fails
|
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4. **🧠 AI Enhancement**: Apply document intelligence and content analysis
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5. **🧹 Clean Output**: Deliver perfectly structured, AI-ready intelligence
|
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|
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---
|
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|
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## 🌍 **Real-World Success Stories**
|
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|
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<div align="center">
|
||||
|
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### **🏢 Proven at Enterprise Scale**
|
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|
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</div>
|
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|
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<table>
|
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<tr>
|
||||
<td>
|
||||
|
||||
### **📊 Financial Services Giant**
|
||||
*Processing 50,000+ reports monthly*
|
||||
|
||||
**Challenge**: Analyze quarterly reports from 2,000+ companies
|
||||
|
||||
**Results**:
|
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- ⚡ **98% time reduction** (2 weeks → 4 hours)
|
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- 🎯 **99.9% accuracy** in financial data extraction
|
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- 💰 **$5M annual savings** in analyst time
|
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- 🏆 **SEC compliance** maintained
|
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|
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</td>
|
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<td>
|
||||
|
||||
### **🏥 Healthcare Research Institute**
|
||||
*Processing 100,000+ research papers*
|
||||
|
||||
**Challenge**: Analyze medical literature for drug discovery
|
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|
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**Results**:
|
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- 🚀 **25x faster** literature review process
|
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- 📋 **95% accuracy** in data extraction
|
||||
- 🧬 **12 new drug targets** identified
|
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- 📚 **Publication in Nature** based on insights
|
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|
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</td>
|
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</tr>
|
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<tr>
|
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<td>
|
||||
|
||||
### **⚖️ Legal Firm Network**
|
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*Processing 500,000+ legal documents*
|
||||
|
||||
**Challenge**: Document review and compliance checking
|
||||
|
||||
**Results**:
|
||||
- 🏃 **40x speed improvement** in document review
|
||||
- 🛡️ **100% security compliance** maintained
|
||||
- 💼 **$20M cost savings** across network
|
||||
- 🏆 **Zero data breaches** during migration
|
||||
|
||||
</td>
|
||||
<td>
|
||||
|
||||
### **🎓 Global University System**
|
||||
*Processing 1M+ academic papers*
|
||||
|
||||
**Challenge**: Create searchable academic knowledge base
|
||||
|
||||
**Results**:
|
||||
- 📖 **50x faster** knowledge extraction
|
||||
- 🧠 **AI-ready** structured academic data
|
||||
- 🔍 **97% search accuracy** improvement
|
||||
- 📊 **3 Nobel Prize** papers processed
|
||||
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
---
|
||||
|
||||
## 🎯 **Advanced Features That Set Us Apart**
|
||||
|
||||
### **🌐 HTTPS URL Processing with Smart Caching**
|
||||
```python
|
||||
# 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!
|
||||
```
|
||||
|
||||
### **🩺 Comprehensive Document Health Analysis**
|
||||
```python
|
||||
# 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
|
||||
}
|
||||
```
|
||||
|
||||
### **🔍 AI-Powered Content Classification**
|
||||
```python
|
||||
# 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"
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🤝 **Perfect Integration Ecosystem**
|
||||
|
||||
### **💎 Companion to MCP Office Tools**
|
||||
*The ultimate document processing powerhouse*
|
||||
|
||||
<div align="center">
|
||||
|
||||
| 🔧 **Processing Need** | 📄 **PDF Files** | 📊 **Office Files** | 🔗 **Integration** |
|
||||
|-----------------------|------------------|-------------------|-------------------|
|
||||
| **Text Extraction** | MCP PDF ✅ | [MCP Office Tools](https://git.supported.systems/MCP/mcp-office-tools) ✅ | **Unified API** |
|
||||
| **Table Processing** | Advanced ✅ | Advanced ✅ | **Cross-Format** |
|
||||
| **Image Extraction** | Smart ✅ | Smart ✅ | **Consistent** |
|
||||
| **Format Detection** | AI-Powered ✅ | AI-Powered ✅ | **Intelligent** |
|
||||
| **Health Analysis** | Complete ✅ | Complete ✅ | **Comprehensive** |
|
||||
|
||||
[**🚀 Get Both Tools for Complete Document Intelligence**](https://git.supported.systems/MCP/mcp-office-tools)
|
||||
|
||||
</div>
|
||||
|
||||
### **🔗 Unified Document Processing Workflow**
|
||||
```python
|
||||
# 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
|
||||
])
|
||||
```
|
||||
|
||||
### **⚡ Works Seamlessly With**
|
||||
- **🤖 Claude Desktop**: Native MCP protocol integration
|
||||
- **📊 Jupyter Notebooks**: Perfect for research and analysis
|
||||
- **🐍 Python Applications**: Direct async/await API access
|
||||
- **🌐 Web Services**: RESTful wrappers and microservices
|
||||
- **☁️ Cloud Platforms**: AWS Lambda, Google Functions, Azure
|
||||
- **🔄 Workflow Engines**: Zapier, Microsoft Power Automate
|
||||
|
||||
---
|
||||
|
||||
## 🛡️ **Enterprise-Grade Security & Compliance**
|
||||
|
||||
<div align="center">
|
||||
|
||||
| 🔒 **Security Feature** | ✅ **Status** | 📋 **Enterprise Ready** |
|
||||
|------------------------|---------------|------------------------|
|
||||
| **Local Processing** | ✅ Enabled | Documents never leave your environment |
|
||||
| **Memory Security** | ✅ Optimized | Automatic sensitive data cleanup |
|
||||
| **HTTPS Validation** | ✅ Enforced | Certificate validation and secure headers |
|
||||
| **Access Controls** | ✅ Configurable | Role-based processing permissions |
|
||||
| **Audit Logging** | ✅ Available | Complete processing audit trails |
|
||||
| **GDPR Compliant** | ✅ Certified | No personal data retention |
|
||||
| **SOC2 Ready** | ✅ Verified | Enterprise security standards |
|
||||
|
||||
</div>
|
||||
|
||||
---
|
||||
|
||||
## 📈 **Installation & Enterprise Setup**
|
||||
|
||||
<details>
|
||||
<summary>🚀 <b>Quick Start</b> (Recommended)</summary>
|
||||
|
||||
```bash
|
||||
# Clone repository
|
||||
git clone https://github.com/rsp2k/mcp-pdf
|
||||
cd mcp-pdf
|
||||
|
||||
# Install with uv (fastest)
|
||||
uv sync
|
||||
|
||||
# Install system dependencies (Ubuntu/Debian)
|
||||
sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript
|
||||
|
||||
# Verify installation
|
||||
uv run python examples/verify_installation.py
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>🐳 <b>Docker Enterprise Setup</b></summary>
|
||||
|
||||
```dockerfile
|
||||
FROM python:3.11-slim
|
||||
RUN apt-get update && apt-get install -y \
|
||||
tesseract-ocr tesseract-ocr-eng \
|
||||
poppler-utils ghostscript \
|
||||
default-jre-headless
|
||||
COPY . /app
|
||||
WORKDIR /app
|
||||
RUN pip install -e .
|
||||
CMD ["mcp-pdf"]
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>🌐 <b>Claude Desktop Integration</b></summary>
|
||||
|
||||
```json
|
||||
{
|
||||
"mcpServers": {
|
||||
"pdf-tools": {
|
||||
"command": "uv",
|
||||
"args": ["run", "mcp-pdf"],
|
||||
"cwd": "/path/to/mcp-pdf"
|
||||
},
|
||||
"office-tools": {
|
||||
"command": "mcp-office-tools"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
*Unified document processing across all formats!*
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>🔧 <b>Development Environment</b></summary>
|
||||
|
||||
```bash
|
||||
# Clone and setup
|
||||
git clone https://github.com/rsp2k/mcp-pdf
|
||||
cd mcp-pdf
|
||||
uv sync --dev
|
||||
|
||||
# Quality checks
|
||||
uv run pytest --cov=mcp_pdf_tools
|
||||
uv run black src/ tests/ examples/
|
||||
uv run ruff check src/ tests/ examples/
|
||||
uv run mypy src/
|
||||
|
||||
# Run all 23 tools demo
|
||||
# Verify
|
||||
uv run python examples/verify_installation.py
|
||||
```
|
||||
|
||||
@ -563,124 +63,162 @@ uv run python examples/verify_installation.py
|
||||
|
||||
---
|
||||
|
||||
## 🚀 **What's Coming Next?**
|
||||
## Tools
|
||||
|
||||
<div align="center">
|
||||
### Content Extraction
|
||||
|
||||
### **🔮 Innovation Roadmap 2024-2025**
|
||||
| Tool | What it does |
|
||||
|------|-------------|
|
||||
| `extract_text` | Pull text from PDF pages with automatic chunking for large files |
|
||||
| `extract_tables` | Extract tables to JSON, CSV, or Markdown |
|
||||
| `extract_images` | Extract embedded images |
|
||||
| `extract_links` | Get all hyperlinks with page filtering |
|
||||
| `pdf_to_markdown` | Convert PDF to markdown preserving structure |
|
||||
| `ocr_pdf` | OCR scanned documents using Tesseract |
|
||||
| `extract_vector_graphics` | Export vector graphics to SVG (schematics, charts, drawings) |
|
||||
|
||||
### Document Analysis
|
||||
|
||||
| Tool | What it does |
|
||||
|------|-------------|
|
||||
| `extract_metadata` | Get title, author, creation date, page count, etc. |
|
||||
| `get_document_structure` | Extract table of contents and bookmarks |
|
||||
| `analyze_layout` | Detect columns, headers, footers |
|
||||
| `is_scanned_pdf` | Check if PDF needs OCR |
|
||||
| `compare_pdfs` | Diff two PDFs by text, structure, or metadata |
|
||||
| `analyze_pdf_health` | Check for corruption, optimization opportunities |
|
||||
| `analyze_pdf_security` | Report encryption, permissions, signatures |
|
||||
|
||||
### Forms
|
||||
|
||||
| Tool | What it does |
|
||||
|------|-------------|
|
||||
| `extract_form_data` | Get form field names and values |
|
||||
| `fill_form_pdf` | Fill form fields from JSON |
|
||||
| `create_form_pdf` | Create new forms with text fields, checkboxes, dropdowns |
|
||||
| `add_form_fields` | Add fields to existing PDFs |
|
||||
|
||||
### Document Assembly
|
||||
|
||||
| Tool | What it does |
|
||||
|------|-------------|
|
||||
| `merge_pdfs` | Combine multiple PDFs with bookmark preservation |
|
||||
| `split_pdf_by_pages` | Split by page ranges |
|
||||
| `split_pdf_by_bookmarks` | Split at chapter/section boundaries |
|
||||
| `reorder_pdf_pages` | Rearrange pages in custom order |
|
||||
|
||||
### Annotations
|
||||
|
||||
| Tool | What it does |
|
||||
|------|-------------|
|
||||
| `add_sticky_notes` | Add comment annotations |
|
||||
| `add_highlights` | Highlight text regions |
|
||||
| `add_stamps` | Add Approved/Draft/Confidential stamps |
|
||||
| `extract_all_annotations` | Export annotations to JSON |
|
||||
|
||||
---
|
||||
|
||||
## How Fallbacks Work
|
||||
|
||||
The server tries multiple libraries for each operation:
|
||||
|
||||
**Text extraction:**
|
||||
1. PyMuPDF (fastest)
|
||||
2. pdfplumber (better for complex layouts)
|
||||
3. pypdf (most compatible)
|
||||
|
||||
**Table extraction:**
|
||||
1. Camelot (best accuracy, requires Ghostscript)
|
||||
2. pdfplumber (no dependencies)
|
||||
3. Tabula (requires Java)
|
||||
|
||||
If a PDF fails with one library, the next is tried automatically.
|
||||
|
||||
---
|
||||
|
||||
## Token Management
|
||||
|
||||
Large PDFs can overflow MCP response limits. The server handles this:
|
||||
|
||||
- **Automatic chunking** splits large documents into page groups
|
||||
- **Table row limits** prevent huge tables from blowing up responses
|
||||
- **Summary mode** returns structure without full content
|
||||
|
||||
```python
|
||||
# Get first 10 pages
|
||||
result = await extract_text("huge.pdf", pages="1-10")
|
||||
|
||||
# Limit table rows
|
||||
tables = await extract_tables("data.pdf", max_rows_per_table=50)
|
||||
|
||||
# Structure only
|
||||
tables = await extract_tables("data.pdf", summary_only=True)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## URL Processing
|
||||
|
||||
PDFs can be fetched directly from HTTPS URLs:
|
||||
|
||||
```python
|
||||
result = await extract_text("https://example.com/report.pdf")
|
||||
```
|
||||
|
||||
Files are cached locally for subsequent operations.
|
||||
|
||||
---
|
||||
|
||||
## System Dependencies
|
||||
|
||||
Some features require system packages:
|
||||
|
||||
| Feature | Dependency |
|
||||
|---------|-----------|
|
||||
| OCR | `tesseract-ocr` |
|
||||
| Camelot tables | `ghostscript` |
|
||||
| Tabula tables | `default-jre-headless` |
|
||||
| PDF to images | `poppler-utils` |
|
||||
|
||||
Ubuntu/Debian:
|
||||
```bash
|
||||
sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript default-jre-headless
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Configuration
|
||||
|
||||
Optional environment variables:
|
||||
|
||||
| Variable | Purpose |
|
||||
|----------|---------|
|
||||
| `MCP_PDF_ALLOWED_PATHS` | Colon-separated directories for file output |
|
||||
| `PDF_TEMP_DIR` | Temp directory for processing (default: `/tmp/mcp-pdf-processing`) |
|
||||
| `TESSDATA_PREFIX` | Tesseract language data location |
|
||||
|
||||
---
|
||||
|
||||
## Development
|
||||
|
||||
```bash
|
||||
# Run tests
|
||||
uv run pytest
|
||||
|
||||
# With coverage
|
||||
uv run pytest --cov=mcp_pdf
|
||||
|
||||
# Format
|
||||
uv run black src/ tests/
|
||||
|
||||
# Lint
|
||||
uv run ruff check src/ tests/
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
|
||||
</div>
|
||||
|
||||
| 🗓️ **Timeline** | 🎯 **Feature** | 📋 **Impact** |
|
||||
|-----------------|---------------|--------------|
|
||||
| **Q4 2024** | **Enhanced AI Analysis** | GPT-powered content understanding |
|
||||
| **Q1 2025** | **Batch Processing** | Process 1000+ documents simultaneously |
|
||||
| **Q2 2025** | **Cloud Integration** | Direct S3, GCS, Azure Blob support |
|
||||
| **Q3 2025** | **Real-time Streaming** | Process documents as they're created |
|
||||
| **Q4 2025** | **Multi-language OCR** | 50+ language support with AI translation |
|
||||
| **2026** | **Blockchain Verification** | Cryptographic document integrity |
|
||||
|
||||
---
|
||||
|
||||
## 🎭 **Complete Tool Showcase**
|
||||
|
||||
<details>
|
||||
<summary>📊 <b>Business Intelligence Tools</b> (click to expand)</summary>
|
||||
|
||||
### **Core Extraction**
|
||||
- `extract_text` - Multi-method text extraction with layout preservation
|
||||
- `extract_tables` - Intelligent table extraction (JSON, CSV, Markdown)
|
||||
- `extract_images` - Image extraction with size filtering and format options
|
||||
- `pdf_to_markdown` - Clean markdown conversion with structure preservation
|
||||
|
||||
### **AI-Powered Analysis**
|
||||
- `classify_content` - AI document type classification and analysis
|
||||
- `summarize_content` - Intelligent summarization with key insights
|
||||
- `analyze_pdf_health` - Comprehensive quality assessment
|
||||
- `analyze_pdf_security` - Security feature analysis and vulnerability detection
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>🔍 <b>Advanced Analysis Tools</b> (click to expand)</summary>
|
||||
|
||||
### **Document Intelligence**
|
||||
- `compare_pdfs` - Advanced document comparison (text, structure, metadata)
|
||||
- `is_scanned_pdf` - Smart detection of scanned vs. text-based documents
|
||||
- `get_document_structure` - Document outline and structural analysis
|
||||
- `extract_metadata` - Comprehensive metadata and statistics extraction
|
||||
|
||||
### **Visual Processing**
|
||||
- `analyze_layout` - Page layout analysis with column and spacing detection
|
||||
- `extract_charts` - Chart, diagram, and visual element extraction
|
||||
- `detect_watermarks` - Watermark detection and analysis
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>🔨 <b>Document Manipulation Tools</b> (click to expand)</summary>
|
||||
|
||||
### **Content Operations**
|
||||
- `extract_form_data` - Interactive PDF form data extraction
|
||||
- `split_pdf` - Intelligent document splitting at specified pages
|
||||
- `merge_pdfs` - Multi-document merging with page range tracking
|
||||
- `rotate_pages` - Precise page rotation (90°/180°/270°)
|
||||
|
||||
### **Optimization & Repair**
|
||||
- `convert_to_images` - PDF to image conversion with quality control
|
||||
- `optimize_pdf` - Multi-level file size optimization
|
||||
- `repair_pdf` - Automated corruption repair and recovery
|
||||
- `ocr_pdf` - Advanced OCR with preprocessing for scanned documents
|
||||
|
||||
</details>
|
||||
|
||||
---
|
||||
|
||||
## 💝 **Enterprise Support & Community**
|
||||
|
||||
<div align="center">
|
||||
|
||||
### **🌟 Join the PDF Intelligence Revolution!**
|
||||
|
||||
[](https://github.com/rsp2k/mcp-pdf)
|
||||
[](https://github.com/rsp2k/mcp-pdf/issues)
|
||||
[](https://git.supported.systems/MCP/mcp-office-tools)
|
||||
|
||||
**💬 Enterprise Support Available** • **🐛 Bug Bounty Program** • **💡 Feature Requests Welcome**
|
||||
|
||||
</div>
|
||||
|
||||
### **🏢 Enterprise Services**
|
||||
- **📞 Priority Support**: 24/7 enterprise support available
|
||||
- **🎓 Training Programs**: Comprehensive team training
|
||||
- **🔧 Custom Integration**: Tailored enterprise deployments
|
||||
- **📊 Analytics Dashboard**: Usage analytics and insights
|
||||
- **🛡️ Security Audits**: Comprehensive security assessments
|
||||
|
||||
---
|
||||
|
||||
<div align="center">
|
||||
|
||||
## 📜 **License & Ecosystem**
|
||||
|
||||
**MIT License** - Freedom to innovate everywhere
|
||||
|
||||
**🤝 Part of the MCP Document Processing Ecosystem**
|
||||
|
||||
*Powered by [FastMCP](https://github.com/jlowin/fastmcp) • [Model Context Protocol](https://modelcontextprotocol.io) • Enterprise Python*
|
||||
|
||||
### **🔗 Complete Document Processing Solution**
|
||||
|
||||
**PDF Intelligence** ➜ **[MCP PDF](https://github.com/rsp2k/mcp-pdf)** (You are here!)
|
||||
**Office Intelligence** ➜ **[MCP Office Tools](https://git.supported.systems/MCP/mcp-office-tools)**
|
||||
**Unified Power** ➜ **Both Tools Together**
|
||||
|
||||
---
|
||||
|
||||
### **⭐ Star both repositories for the complete solution! ⭐**
|
||||
|
||||
**📄 [Star MCP PDF](https://github.com/rsp2k/mcp-pdf)** • **📊 [Star MCP Office Tools](https://git.supported.systems/MCP/mcp-office-tools)**
|
||||
|
||||
*Building the future of intelligent document processing* 🚀
|
||||
|
||||
</div>
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "mcp-pdf"
|
||||
version = "2.0.7"
|
||||
version = "2.0.8"
|
||||
description = "Secure FastMCP server for comprehensive PDF processing - text extraction, OCR, table extraction, forms, annotations, and more"
|
||||
authors = [{name = "Ryan Malloy", email = "ryan@malloys.us"}]
|
||||
readme = "README.md"
|
||||
|
||||
@ -382,4 +382,358 @@ class ImageProcessingMixin(MCPMixin):
|
||||
"""Simple heuristic to detect if line contains intentional markdown formatting"""
|
||||
# Very basic check - could be enhanced
|
||||
markdown_patterns = ['# ', '## ', '### ', '* ', '- ', '1. ', '**', '__']
|
||||
return any(pattern in line for pattern in markdown_patterns)
|
||||
return any(pattern in line for pattern in markdown_patterns)
|
||||
|
||||
@mcp_tool(
|
||||
name="extract_vector_graphics",
|
||||
description="Extract vector graphics from PDF to SVG format. Ideal for schematics, charts, and technical drawings."
|
||||
)
|
||||
async def extract_vector_graphics(
|
||||
self,
|
||||
pdf_path: str,
|
||||
output_directory: Optional[str] = None,
|
||||
pages: Optional[str] = None,
|
||||
mode: str = "full_page",
|
||||
include_text: bool = True,
|
||||
simplify_paths: bool = False,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Extract vector graphics from PDF pages as SVG files.
|
||||
|
||||
Perfect for extracting:
|
||||
- IC functional diagrams from datasheets
|
||||
- Frequency response charts and line graphs
|
||||
- Package outline drawings (dimensioned technical drawings)
|
||||
- Circuit schematics
|
||||
- PCB layout diagrams
|
||||
|
||||
Args:
|
||||
pdf_path: Path to PDF file or HTTPS URL
|
||||
output_directory: Directory to save SVG files (default: temp directory)
|
||||
pages: Page numbers to extract (comma-separated, 1-based), None for all
|
||||
mode: Extraction mode:
|
||||
- "full_page": Complete page as SVG (default, best for general use)
|
||||
- "drawings_only": Extract individual vector paths as separate SVG
|
||||
- "both": Export both formats for flexibility
|
||||
include_text: Whether to include text in SVG output (default: True)
|
||||
simplify_paths: Reduce path complexity for smaller files (default: False)
|
||||
|
||||
Returns:
|
||||
Dictionary containing extraction summary and SVG file paths
|
||||
"""
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
# Validate PDF path
|
||||
input_pdf_path = await validate_pdf_path(pdf_path)
|
||||
|
||||
# Setup output directory
|
||||
if output_directory:
|
||||
output_dir = validate_output_path(output_directory)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
else:
|
||||
output_dir = Path(tempfile.mkdtemp(prefix="pdf_vectors_"))
|
||||
|
||||
# Parse pages parameter
|
||||
parsed_pages = parse_pages_parameter(pages)
|
||||
|
||||
# Validate mode
|
||||
valid_modes = ["full_page", "drawings_only", "both"]
|
||||
if mode not in valid_modes:
|
||||
return {
|
||||
"success": False,
|
||||
"error": f"Invalid mode '{mode}'. Valid modes: {', '.join(valid_modes)}",
|
||||
"extraction_time": round(time.time() - start_time, 2)
|
||||
}
|
||||
|
||||
# Open PDF document
|
||||
doc = fitz.open(str(input_pdf_path))
|
||||
total_pages = len(doc)
|
||||
|
||||
# Determine pages to process
|
||||
pages_to_process = parsed_pages if parsed_pages else list(range(total_pages))
|
||||
pages_to_process = [p for p in pages_to_process if 0 <= p < total_pages]
|
||||
|
||||
if not pages_to_process:
|
||||
doc.close()
|
||||
return {
|
||||
"success": False,
|
||||
"error": "No valid pages specified",
|
||||
"extraction_time": round(time.time() - start_time, 2)
|
||||
}
|
||||
|
||||
svg_files = []
|
||||
total_size = 0
|
||||
base_name = input_pdf_path.stem
|
||||
|
||||
for page_num in pages_to_process:
|
||||
try:
|
||||
page = doc[page_num]
|
||||
page_results = {}
|
||||
|
||||
# Full page SVG extraction
|
||||
if mode in ["full_page", "both"]:
|
||||
svg_content = page.get_svg_image(
|
||||
text_as_path=not include_text
|
||||
)
|
||||
|
||||
# Optionally simplify paths (basic implementation)
|
||||
if simplify_paths:
|
||||
svg_content = self._simplify_svg_paths(svg_content)
|
||||
|
||||
filename = f"{base_name}_page_{page_num + 1}.svg"
|
||||
output_path = output_dir / filename
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(svg_content)
|
||||
|
||||
file_size = output_path.stat().st_size
|
||||
total_size += file_size
|
||||
|
||||
page_results["full_page"] = {
|
||||
"filename": filename,
|
||||
"path": str(output_path),
|
||||
"size_bytes": file_size,
|
||||
"size_kb": round(file_size / 1024, 1)
|
||||
}
|
||||
|
||||
# Individual drawings extraction
|
||||
if mode in ["drawings_only", "both"]:
|
||||
drawings = page.get_drawings()
|
||||
drawing_count = len(drawings)
|
||||
|
||||
if drawing_count > 0:
|
||||
# Convert drawings to SVG
|
||||
drawings_svg = self._drawings_to_svg(
|
||||
drawings,
|
||||
page.rect.width,
|
||||
page.rect.height
|
||||
)
|
||||
|
||||
filename = f"{base_name}_page_{page_num + 1}_drawings.svg"
|
||||
output_path = output_dir / filename
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(drawings_svg)
|
||||
|
||||
file_size = output_path.stat().st_size
|
||||
total_size += file_size
|
||||
|
||||
page_results["drawings_only"] = {
|
||||
"filename": filename,
|
||||
"path": str(output_path),
|
||||
"size_bytes": file_size,
|
||||
"size_kb": round(file_size / 1024, 1),
|
||||
"drawing_count": drawing_count
|
||||
}
|
||||
else:
|
||||
page_results["drawings_only"] = {
|
||||
"skipped": True,
|
||||
"reason": "No vector drawings found on page"
|
||||
}
|
||||
|
||||
# Get drawing statistics for the page
|
||||
all_drawings = page.get_drawings()
|
||||
|
||||
svg_files.append({
|
||||
"page": page_num + 1,
|
||||
"has_text": bool(page.get_text().strip()),
|
||||
"drawing_count": len(all_drawings),
|
||||
**page_results
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to extract vectors from page {page_num + 1}: {e}")
|
||||
svg_files.append({
|
||||
"page": page_num + 1,
|
||||
"error": sanitize_error_message(str(e))
|
||||
})
|
||||
|
||||
doc.close()
|
||||
|
||||
# Count successful extractions
|
||||
successful_pages = sum(1 for f in svg_files if "error" not in f)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"extraction_summary": {
|
||||
"pages_processed": len(pages_to_process),
|
||||
"pages_successful": successful_pages,
|
||||
"mode": mode,
|
||||
"total_size_bytes": total_size,
|
||||
"total_size_kb": round(total_size / 1024, 1),
|
||||
"output_directory": str(output_dir)
|
||||
},
|
||||
"svg_files": svg_files,
|
||||
"settings": {
|
||||
"include_text": include_text,
|
||||
"simplify_paths": simplify_paths,
|
||||
"mode": mode
|
||||
},
|
||||
"file_info": {
|
||||
"input_path": str(input_pdf_path),
|
||||
"total_pages": total_pages,
|
||||
"pages_processed": pages or "all"
|
||||
},
|
||||
"extraction_time": round(time.time() - start_time, 2),
|
||||
"hints": {
|
||||
"viewing": "Open SVG files in browser, Inkscape, or Illustrator for editing",
|
||||
"full_page_vs_drawings": "full_page preserves layout; drawings_only extracts raw vector paths"
|
||||
}
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = sanitize_error_message(str(e))
|
||||
logger.error(f"Vector graphics extraction failed: {error_msg}")
|
||||
return {
|
||||
"success": False,
|
||||
"error": error_msg,
|
||||
"extraction_time": round(time.time() - start_time, 2)
|
||||
}
|
||||
|
||||
def _drawings_to_svg(
|
||||
self,
|
||||
drawings: List[Dict],
|
||||
width: float,
|
||||
height: float
|
||||
) -> str:
|
||||
"""
|
||||
Convert PyMuPDF drawings to standalone SVG.
|
||||
|
||||
Drawings contain: rect, items (path operations), color, fill, width, etc.
|
||||
"""
|
||||
svg_parts = [
|
||||
f'<?xml version="1.0" encoding="UTF-8"?>',
|
||||
f'<svg xmlns="http://www.w3.org/2000/svg" ',
|
||||
f'viewBox="0 0 {width:.2f} {height:.2f}" ',
|
||||
f'width="{width:.2f}" height="{height:.2f}">',
|
||||
'',
|
||||
' <!-- Extracted vector drawings from PDF -->',
|
||||
''
|
||||
]
|
||||
|
||||
for idx, drawing in enumerate(drawings):
|
||||
try:
|
||||
path_data = self._drawing_to_path(drawing)
|
||||
if not path_data:
|
||||
continue
|
||||
|
||||
# Extract style attributes
|
||||
stroke_color = self._color_to_svg(drawing.get('color'))
|
||||
fill_color = self._color_to_svg(drawing.get('fill'))
|
||||
stroke_width = drawing.get('width', 1)
|
||||
|
||||
# Build style string
|
||||
style_parts = []
|
||||
if fill_color:
|
||||
style_parts.append(f'fill:{fill_color}')
|
||||
else:
|
||||
style_parts.append('fill:none')
|
||||
|
||||
if stroke_color:
|
||||
style_parts.append(f'stroke:{stroke_color}')
|
||||
style_parts.append(f'stroke-width:{stroke_width:.2f}')
|
||||
|
||||
style = ';'.join(style_parts)
|
||||
|
||||
svg_parts.append(f' <path d="{path_data}" style="{style}" />')
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"Failed to convert drawing {idx}: {e}")
|
||||
continue
|
||||
|
||||
svg_parts.append('</svg>')
|
||||
return '\n'.join(svg_parts)
|
||||
|
||||
def _drawing_to_path(self, drawing: Dict) -> Optional[str]:
|
||||
"""Convert a single drawing to SVG path data string."""
|
||||
items = drawing.get('items', [])
|
||||
if not items:
|
||||
return None
|
||||
|
||||
path_parts = []
|
||||
|
||||
for item in items:
|
||||
if not item:
|
||||
continue
|
||||
|
||||
# Item format: (type, points...)
|
||||
item_type = item[0]
|
||||
|
||||
try:
|
||||
if item_type == 'l': # Line
|
||||
# ('l', Point, Point)
|
||||
p1, p2 = item[1], item[2]
|
||||
path_parts.append(f'M {p1.x:.2f} {p1.y:.2f}')
|
||||
path_parts.append(f'L {p2.x:.2f} {p2.y:.2f}')
|
||||
|
||||
elif item_type == 're': # Rectangle
|
||||
# ('re', Rect)
|
||||
rect = item[1]
|
||||
path_parts.append(f'M {rect.x0:.2f} {rect.y0:.2f}')
|
||||
path_parts.append(f'L {rect.x1:.2f} {rect.y0:.2f}')
|
||||
path_parts.append(f'L {rect.x1:.2f} {rect.y1:.2f}')
|
||||
path_parts.append(f'L {rect.x0:.2f} {rect.y1:.2f}')
|
||||
path_parts.append('Z')
|
||||
|
||||
elif item_type == 'qu': # Quad (4-point polygon)
|
||||
# ('qu', Quad)
|
||||
quad = item[1]
|
||||
path_parts.append(f'M {quad.ul.x:.2f} {quad.ul.y:.2f}')
|
||||
path_parts.append(f'L {quad.ur.x:.2f} {quad.ur.y:.2f}')
|
||||
path_parts.append(f'L {quad.lr.x:.2f} {quad.lr.y:.2f}')
|
||||
path_parts.append(f'L {quad.ll.x:.2f} {quad.ll.y:.2f}')
|
||||
path_parts.append('Z')
|
||||
|
||||
elif item_type == 'c': # Cubic bezier curve
|
||||
# ('c', Point, Point, Point, Point) - start, ctrl1, ctrl2, end
|
||||
p0, p1, p2, p3 = item[1], item[2], item[3], item[4]
|
||||
if not path_parts or not path_parts[-1].startswith('M'):
|
||||
path_parts.append(f'M {p0.x:.2f} {p0.y:.2f}')
|
||||
path_parts.append(f'C {p1.x:.2f} {p1.y:.2f} {p2.x:.2f} {p2.y:.2f} {p3.x:.2f} {p3.y:.2f}')
|
||||
|
||||
except (IndexError, AttributeError) as e:
|
||||
logger.debug(f"Failed to process drawing item {item_type}: {e}")
|
||||
continue
|
||||
|
||||
return ' '.join(path_parts) if path_parts else None
|
||||
|
||||
def _color_to_svg(self, color) -> Optional[str]:
|
||||
"""Convert PyMuPDF color to SVG color string."""
|
||||
if color is None:
|
||||
return None
|
||||
|
||||
if isinstance(color, (list, tuple)):
|
||||
if len(color) == 3:
|
||||
r, g, b = [int(c * 255) for c in color]
|
||||
return f'rgb({r},{g},{b})'
|
||||
elif len(color) == 1:
|
||||
# Grayscale
|
||||
gray = int(color[0] * 255)
|
||||
return f'rgb({gray},{gray},{gray})'
|
||||
elif len(color) == 4:
|
||||
# CMYK - convert to RGB (simplified)
|
||||
c, m, y, k = color
|
||||
r = int(255 * (1 - c) * (1 - k))
|
||||
g = int(255 * (1 - m) * (1 - k))
|
||||
b = int(255 * (1 - y) * (1 - k))
|
||||
return f'rgb({r},{g},{b})'
|
||||
|
||||
return None
|
||||
|
||||
def _simplify_svg_paths(self, svg_content: str) -> str:
|
||||
"""
|
||||
Basic SVG path simplification.
|
||||
Reduces decimal precision to shrink file size.
|
||||
"""
|
||||
import re
|
||||
|
||||
# Reduce decimal precision in path data
|
||||
def reduce_precision(match):
|
||||
num = float(match.group())
|
||||
return f'{num:.1f}'
|
||||
|
||||
# Match floating point numbers in SVG
|
||||
simplified = re.sub(r'-?\d+\.\d{3,}', reduce_precision, svg_content)
|
||||
|
||||
return simplified
|
||||
Loading…
x
Reference in New Issue
Block a user