pdf-mcp 1.0.0

Production-ready MCP server for PDF processing with intelligent caching. Extract text, search, and analyze PDFs with AI agents.

pdf-mcp 📄

Production-ready MCP server for PDF processing with intelligent caching.

A Python implementation of the Model Context Protocol (MCP) server that enables AI agents like Claude to read, search, and extract content from PDF files efficiently.

✨ Features

• 🚀 8 Specialized Tools - Purpose-built tools for different PDF operations
• 💾 SQLite Caching - Persistent cache survives server restarts (essential for STDIO transport)
• 📄 Smart Pagination - Read large PDFs in manageable chunks
• 🔍 Full-Text Search - Find content without loading entire document
• 🖼️ Image Extraction - Extract images as base64 PNG
• 🌐 URL Support - Read PDFs from HTTP/HTTPS URLs
• ⚡ Fast Subsequent Access - Cached pages load instantly

📦 Installation

pip install pdf-mcp

🚀 Quick Start

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "pdf": {
      "command": "python",
      "args": ["-m", "pdf_mcp.server"]
    }
  }
}

Location of config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Restart Claude Desktop

After updating the config, restart Claude Desktop to load the MCP server.

🛠️ Tools

1. pdf_info - Get Document Information

Always call this first to understand the document before reading.

"Read the PDF at /path/to/document.pdf"

Returns: page count, metadata, table of contents, file size, estimated tokens.

2. pdf_read_pages - Read Specific Pages

Read pages in chunks to manage context size.

"Read pages 1-10 of the PDF"
"Read pages 15, 20, and 25-30"

3. pdf_read_all - Read Entire Document

For small documents only (has safety limit).

"Read the entire PDF (it's only 10 pages)"

4. pdf_search - Search Within PDF

Find relevant pages before loading content.

"Search for 'quarterly revenue' in the PDF"

5. pdf_get_toc - Get Table of Contents

"Show me the table of contents"

6. pdf_extract_images - Extract Images

"Extract images from pages 1-5"

7. pdf_cache_stats - View Cache Statistics

"Show PDF cache statistics"

8. pdf_cache_clear - Clear Cache

"Clear expired PDF cache entries"

📋 Example Workflow

For a large document (e.g., 200-page annual report):

User: "Summarize the risk factors in this annual report"

Claude's workflow:
1. pdf_info("report.pdf") 
   → Learns: 200 pages, TOC shows "Risk Factors" on page 89

2. pdf_search("report.pdf", "risk factors")
   → Finds relevant pages: 89-110

3. pdf_read_pages("report.pdf", "89-100")
   → Reads first batch

4. pdf_read_pages("report.pdf", "101-110")
   → Reads second batch

5. Synthesizes answer from chunks

💾 Caching

The server uses SQLite for persistent caching because MCP with STDIO transport spawns a new process for each conversation.

Cache Location

• ~/.cache/pdf-mcp/cache.db

What's Cached

Data	Benefit
Metadata	Instant document info
Page text	Skip re-extraction
Images	Skip re-encoding
TOC	Fast navigation

Cache Invalidation

• Automatic when file modification time changes
• Manual via pdf_cache_clear tool
• TTL: 24 hours (configurable)

⚙️ Configuration

Environment variables:

# Cache directory (default: ~/.cache/pdf-mcp)
PDF_MCP_CACHE_DIR=/path/to/cache

# Cache TTL in hours (default: 24)
PDF_MCP_CACHE_TTL=48

🔧 Development

# Clone
git clone https://github.com/jztan/pdf-mcp.git
cd pdf-mcp

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

# Type checking
mypy src/

# Linting
ruff check src/

📊 Comparison

Feature	Traditional Approach	pdf-mcp
Large PDFs	Context overflow	Chunked reading
Repeated access	Re-parse every time	SQLite cache
Find content	Load everything	Search first
Multiple tools	One monolithic tool	8 specialized tools

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - see LICENSE file.

🔗 Links

• PyPI Package
• MCP Documentation
• GitHub Repository

---

Built with ❤️ for the AI agent ecosystem