"A tool for reading PDF files" matching MCP servers:

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.

An MCP server that provides a tool to extract text content from local PDF files, supporting both standard PDF reading and OCR capabilities with optional page selection.

An MCP server that enables file system search and inspection, including directory listing, regex-based file name and content searches, and reading text, PDF, and DOCX files.

A Model Context Protocol server that lets AI clients read and process PDF files locally on your Mac without uploading them anywhere, ensuring privacy.

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.

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

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.

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

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.

Enables reading, searching, and metadata extraction from PDF files without loading the entire content into the context window. It provides efficient tools for text cleaning, page-specific extraction, and context-aware search results.

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

Enables AI agents to securely read and extract information from PDF files including text content, metadata, and page counts from both local files and URLs within the project context.

Enables reading and extracting content from PDF documents including text (as Markdown), images, tables, and metadata from both local files and URLs, with OCR support for scanned documents.

An MCP server that converts Markdown files containing Mermaid diagrams into PDF documents by rendering diagrams as SVG images. It provides a specialized tool to automate document conversion while ensuring all visual charts are correctly embedded in the final output.

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.

Enables AI assistants to read, create, merge, split, and edit PDF files through natural language commands in GitHub Copilot Chat.

A server that provides tools for reading and processing PDF documents, allowing users to list available PDFs and extract their content in Markdown format.

mcp using PyPDF2 to: • merge-pdfs • extract-pages • search-pdfs • merge-pdfs-ordered (merge in user spec. order) • find-related-pdfs (regex extracted text for related PDF files)

A lightweight MCP server for basic file operations, enabling reading, writing, and listing files securely via the Model Context Protocol.