"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

Enables PDF processing and analysis including text extraction, metadata retrieval, search, page manipulation, splitting/merging, conversion to images, and form handling.

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.

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.

MCP server for PDF manipulation — create PDFs from Markdown with tables and formatting, fill forms, merge, split, encrypt, add QR codes, and more. 16 tools, zero external binaries, TypeScript-native. Install with npx -y @aryanbv/pdf-toolkit-mcp.

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

Enables assistants to search a Zotero library, retrieve metadata and PDF text, and view PDF pages as rendered images so figures and layouts are visible.

This MCP server enables AI agents to view PDFs as accessible HTML with bounding-box citations, and provides tools for layout-aware parsing, schema extraction, cross-document Q&A, and PDF rendering.

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.

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

A Model Context Protocol server that enables AI agents to read, search, and extract content from PDF files.

An MCP server that enables users to read, search, and analyze PDF documents. It provides tools for extracting text, viewing metadata, searching content with context, and generating word statistics.

A Model Context Protocol server that extracts and processes content from PDF documents, providing text extraction, metadata retrieval, page-level processing, and PDF validation capabilities.

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

Enables PDF reading through MCP with text extraction, Chinese OCR for scanned documents, page rendering, and keyword file search from any compatible MCP client.

Interactive PDF workspace shared by a human operator and an AI agent, enabling navigation, search, text extraction, annotations, form filling, and save actions via a PDF.js viewer.

pdf-tools-mcp

An MCP server for PDF form filling, basic editing, and OCR text extraction. It enables users to merge, rotate, annotate, and sign PDFs, while also supporting text extraction from both searchable and scanned image-based documents.