Search large PDFs and read only the relevant pages before answering

Use pdf-mcp to inspect a PDF, search it, and load only the pages that matter so an agent can answer questions from long documents without brute-forcing the whole file into context.

Prerequisites

Python 3.10+; an MCP-compatible client; local PDFs or accessible PDF URLs; optional extra dependencies for semantic search.

Installation

Use the upstream install or setup path that matches your environment:

• pip install pdf-mcp
• pip install 'pdf-mcp[semantic]'
• brew install tesseract
• git clone https://github.com/jztan/pdf-mcp.git

Requirements and caveats from upstream:

• 
• A Model Context Protocol (MCP) server that enables AI agents to read, search, and extract content from PDF files. Built with Python and PyMuPDF, with SQLite-based caching for persis...
• For OCR on scanned PDFs (requires system Tesseract):

Basic usage or getting-started notes:

• bash

• For semantic search (adds fastembed and numpy, ~67 MB model download on first use):

• macOS

• Source: https://github.com/jztan/pdf-mcp

• Extracted from upstream docs: https://raw.githubusercontent.com/jztan/pdf-mcp/HEAD/README.md

Documentation

• https://github.com/jztan/pdf-mcp

Source

• Agent Skill Exchange