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 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 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.
A server that provides tools for reading and processing PDF documents, allowing users to list available PDFs and extract their content in Markdown format.
A local-first MCP server that ingests PDFs, extracts structure, and provides semantic search and sequential navigation tools for AI clients to query and learn from documents.
