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

Enables document conversion between PDF, DOCX, and Markdown formats to facilitate reading and editing complex files in AI tools like Claude Desktop or Cursor. It utilizes marker-pdf and pandoc to provide structured text versions of documents, helping to manage context and support unsupported file types.

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 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.

An MCP server for reading, creating, and managing PDF, DOCX, and Excel documents with OCR, styling, categorization, and remote file bridges.

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.

Enables AI agents to read documents in Excel, DOCX, PDF, and TXT formats via MCP protocol.

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

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 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 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 multi-format MCP server that enables reading and generating Office, PDF, text, EPUB, and presentation documents. It provides unified tools for document processing and creation through AI assistants.

Enables PDF generation from HTML, text, and Markdown content with customizable formatting options. Provides secure cross-platform PDF creation tools that automatically save to user directories like Downloads, Documents, or Desktop.

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.

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 Model Context Protocol server that enables listing and reading PDF documents from a configured directory, extracting text content for MCP clients like Claude Desktop.