PDF Agent MCP

- Go toClaude > Settings > Extensions > Advanced Settings
- Disable"Use Built-in Node.js for MCP"
- Restart Claude Desktop

This extension will NOT work with Claude's built-in Node.js. You must use your system's Node.js installation.

If you experience issues loading the extension:
- Verify Node.js is installed: Runnode --versionin your terminal
- Ensure "Use Built-in Node.js for MCP" is disabled in Claude Desktop settings
- Restart Claude Desktop completely
- Check the logs at~/Library/Logs/Claude/mcp-server-PDF Agent MCP.log(macOS) or%LOCALAPPDATA%\Claude\Logs\mcp-server-PDF Agent MCP.log(Windows)

A Model Context Protocol server designed for agentic reading and selective PDF processing. Enables AI systems to efficiently navigate and extract content from PDFs without overwhelming context windows.

- Metadata Extraction: Get PDF properties, page count, and file information
- Text Extraction: Native text extraction with hybrid processing for better results
- Image Conversion: Convert PDF pages to optimized images for visual analysis
- Content Search: Pattern/regex search with context snippets
- Table of Contents: Extract bookmarks and document outline
- Flexible Path Support: Use absolute paths or relative paths from~/pdf-agent/

PDF Agent MCP solves the common problem of context window overflow when working with PDFs in AI tools.

Important: Do not drag PDFs into the chat- this will load the entire PDF content traditionally and bypass the intelligent processing. Instead, provide file paths or URLs to activate the PDF Agent tools for selective processing.

- Provide the absolute file path to your PDF
- Quick tip: Right-click your PDF → "Open with Chrome" → copy the address bar URL for the absolute path

- Simply provide the PDF URL - the agent will download and process it locally

- Selective Reading: The AI first examines metadata and outline, then opens only relevant pages
- Token Efficiency: Avoids images when possible, uses them only when necessary for visual analysis
- Scalable: Works with large documents (1000+ page textbooks) and multiple PDFs simultaneously
- Search Capability: Built-in pattern/regex search across PDF content

This MCP usesagentic search with simple toolsrather than complex alternatives:

- No embedding creation, chunking, or vector storage required
- No multi-agent coordination or handoff complexity
- Just clean, effective tools that modern AI systems can use intelligently

Perfect for researchers, students, and professionals working with extensive PDF libraries.

Copy this prompt into your AI assistant's custom instructions or context for best results:

When working with PDFs using the PDF Agent MCP tools, follow this strategic approach: ### 1. Query Analysis & PDF Identification - Think carefully about the user's search query and information needs - Identify which PDF(s) are most likely to contain the answer - Consider the document type, domain, and likely structure based on the query ### 2. Exploratory Phase (Always Start Here) - Get metadata first using get_pdf_metadata to understand document size, creation date, and properties - Extract table of contents with get_pdf_outline to understand document structure and navigation - Analyze the outline to identify which sections are most relevant to the query ### 3. Strategic Content Extraction Based on the outline and metadata: - Use page ranges ("5:10", "20:") to focus on specific sections rather than entire documents - Extract images with get_pdf_images when visual content is critical (charts, diagrams, tables, equations) - Choose text extraction strategy: hybrid (default) for most cases, native for clean PDFs, ocr for scanned documents ### 4. Advanced Search Strategies - Use multiple search queries with different keywords and synonyms - Apply regex patterns for flexible matching: /budget|cost|expense/gi instead of single terms - Combine searches: Start broad, then narrow down with specific terms - Use context characters (150+ chars) to understand search result context - Implement early stopping with max_results for large documents ### 5. Iterative Refinement - Start with targeted searches based on outline analysis - Follow up with broader searches if initial queries don't yield results - Extract specific page ranges identified through search results - Use visual analysis (images) when text extraction seems incomplete or when layout matters ### 6. Performance Optimization - Avoid processing entire large PDFs - always use page ranges when possible - Use search with early stopping before extracting large sections - Prefer search over full text extraction for finding specific information - Extract images selectively only when visual analysis is needed ### 7. Multi-Document Workflows - Process documents in parallel when comparing multiple PDFs - Use consistent search terms across documents for comparison - Combine results strategically rather than processing everything at once ### Key Principles: - Strategic before comprehensive: Understand document structure before diving deep - Search before extract: Use pattern matching to locate relevant content first - Visual when necessary: Extract images only when text extraction is insufficient - Iterative refinement: Start targeted, expand scope as needed - Context preservation: Always maintain enough context around search results This approach maximizes efficiency, minimizes token usage, and provides more accurate, focused results than traditional "dump entire PDF" methods.

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