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

Enables retrieval of compact APG pattern cards, such as button, modal, and dialog, through an MCP tool so clients can consult accessibility guidance.

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

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

Enables AI-powered extraction and analysis of PDF documents with 40+ specialized tools for text, tables, images, layout analysis, security assessment, and document intelligence. Supports both text-based and scanned PDFs with OCR capabilities.

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.

Enables precise extraction of PDF content, including text coordinates, vector shapes, images, and on-demand raster rendering, for use cases like reconstructing design files.

A PDF MCP server for extracting text, rendered pages, tables, links, annotations, outlines, metadata, and text statistics from PDF files.

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 agents to read, search, and render PDFs confined to a single root folder, with hidden-text warnings and options to merge, split, and create PDFs from Markdown. Read-only by default, with writing tools offered only when explicitly enabled.

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 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.

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 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.

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 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.

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.

Enables AI agents to parse and extract text, metadata, and statistics from PDF documents via URL, with x402 micropayments on Base Mainnet.

pdf-tools-mcp

Enables MCP clients to parse charts and chart data out of PDF documents. The deployed service requires a bearer token on every request and rejects any tool argument that the tool's signature does not declare.

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.

MCP server for parsing PDF files, enabling Cursor IDE to extract text, metadata, and validate PDFs through Docker.