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

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.

MCP server for full-text search across PDF document collections with offline indexing, ranked results, snippets, and page rendering.

Enables AI assistants to natively read, edit, and redact PDF documents in place through MCP tools, eliminating manual script execution and layout issues.

Enables MCP clients to inspect PDF structure and extract object-level data such as characters, words, tables, images, fonts, and metadata, with JSON/CSV export and image retrieval.

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.

Provides 18 PDF operations (merge, split, convert, edit, secure, repair, OCR) via iLoveAPI, callable from any MCP client like Claude Code or Claude Desktop.

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.

Create, merge, split and fill PDF files on your machine from Claude, ChatGPT, Cursor or any MCP client, with no upload.

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 for local PDF manipulation including merging, splitting, rotating, watermarking, and text extraction. It works with various MCP-compatible clients and processes PDFs entirely on-device without cloud services.

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)

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

Enables filling any PDF form, including scanned or AcroForm, through a browser-based drag-and-drop editor. Works entirely locally with no data leaving the machine.

The local PDF workflow for Claude Desktop and MCP hosts: fill, sign, merge, split, extract, and analyze PDFs without sending files to a web app.

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.

An MCP server that provides tools for reading, writing, and manipulating PDF files, including text extraction, metadata retrieval, and merging or splitting documents. It also enables users to create PDFs from plain text and convert specific pages or entire documents into images.

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.

It enables AI agents and MCP clients to read, fill, create, export, and reconstruct fillable PDF forms, plus extract, search, and edit text and layout blocks, entirely against local files over stdio.

A local stdio MCP server that reads a PDF page from a local path or authorized download URL and renders it in memory, returning a real PNG image block plus page metadata. This lets an agent's vision model actually see handwriting, formulas, arrows, colors, and figures instead of only extracted text, with configurable DPI and clipping while reporting page count and size.

Enables MCP clients to audit local PDFs for accessibility issues, batch-audit folders, explain findings, compare reports, and render self-contained HTML, JSON, or Markdown reports without uploading documents or calling models.