"A tool or method for searching PDF documents" matching MCP servers:

A production-ready Model Context Protocol server that bridges local document management with cloud synchronization (Notion) for AI agent integration, enabling seamless access and sync of local and cloud documents.

An MCP server that provides tools for reading, writing, and manipulating PDF files, including text extraction, metadata retrieval, and merging or splitting documents. It also enables users to create PDFs from plain text and convert specific pages or entire documents into images.

An MCP server that exposes Marker for high-quality PDF OCR and conversion, providing a tool to convert PDFs to markdown, JSON, or HTML with optional LLM-enhanced mode.

This server converts PDFs to markdown with intelligent academic paper detection and dual processing engines (marker-pdf for academic content, PyMuPDF for general documents), supporting URL and local file handling, page extraction, batch processing, and PDF link crawling for Claude Code analysis.

An MCP server for local PDF inspection that classifies, searches, extracts text and tables without uploading documents or relying on OCR, surfacing reliability warnings for garbled or scanned content.

MCP server for full-text search across PDF document collections with offline indexing, ranked results, snippets, and page rendering.

Enables AI assistants to natively read, edit, and redact PDF documents in place through MCP tools, eliminating manual script execution and layout issues.

Extracts images from PDF files, allowing LLMs to access and analyze embedded images from local PDF documents.

An MCP server for reading, rendering, and searching PDF files, specifically optimized for LLMs to extract text, tables, and technical diagrams. It enables metadata retrieval, multi-format text extraction, and page-to-image rendering using PyMuPDF.

Provides 18 PDF operations (merge, split, convert, edit, secure, repair, OCR) via iLoveAPI, callable from any MCP client like Claude Code or Claude Desktop.

Memory-efficient MCP server for reading and splitting large PDF files page by page using PyMuPDF.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

An MCP server that enables AI assistants to read, search, and analyze PDF files from local paths or URLs. It provides tools for extracting specific page ranges, searching for terms, and retrieving document metadata.

An MCP server that splits PDFs by chapters/sections and reads them in Claude-friendly chunks. Enables structured reading of large PDF documents.

Provides PDF reading, text search, navigation, and form filling capabilities through MCP, with cross-platform PDF viewer integration for seamless document interaction.

A comprehensive tool server for reading, merging, and extracting content from PDF files via local paths or direct URLs. It enables metadata retrieval, regex searching, and page-specific text extraction with built-in caching and workspace-restricted security.

A Model Context Protocol server that enables the extraction of text, metadata, and embedded images from PDF files. It provides tools for searching text with context, reading specific pages, and counting total pages within a document.

A Model Context Protocol server that enables AI assistants to read PDF files, extract text, retrieve metadata, read specific pages, and search within PDFs.

An MCP server for local PDF manipulation including merging, splitting, rotating, watermarking, and text extraction. It works with various MCP-compatible clients and processes PDFs entirely on-device without cloud services.

A server designed for processing PDF documents, enabling text extraction, table data retrieval, and metadata collection from local files. It allows users to scan directories for PDFs and read specific pages, specifically optimized for thesis literature analysis.