"A tool for reading and extracting content from PDF papers" matching MCP servers:

A local MCP server for low-token co-reading: import a book, split it into chapter-sized chunks, read it chunk by chunk, and keep notes so a session can resume without re-reading the whole book.

Enables a shared reading room where users and Claude can import EPUB or text books, read chunk-by-chunk, annotate passages, search, track progress, and resume from unread sections.

A mobile-first AI co-reading MCP server that lets users import novels or comics, sync reading progress with AI, and interact through comments, bookmarks, and notes.

Enables shared reading between humans and AI assistants, including book import, chunked reading, margin annotations, reading progress tracking, and note exchange.

An AI reading companion that curates next books based on the Chicago Plan curriculum and tracks user reading progress with personal tokens. It offers book recommendations, logging, coaching questions, and reading reports.

Extract content from URLs, documents, videos, and audio files using intelligent auto-engine selection. Supports web pages, PDFs, Word docs, YouTube transcripts, and more with structured JSON responses.

A local MCP server that gives Claude a durable reading room for EPUB and plain text books, enabling chunk-by-chunk navigation, annotations, progress tracking, and shared margin cards.

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

Unified academic paper search MCP server integrating OpenAlex, Semantic Scholar, CrossRef, and arXiv APIs, enabling Claude to search and analyze over 500 million scholarly papers.

Enables agents to discover how a website serves its data, verify sources with evidence, export OpenAPI 3.1, and generate tested TypeScript or Python clients.

Enables AI agents to analyze a piece of content across 11 dimensions and receive ranked, scored recommendations among six cross-media output formats (PPT, infographic, comic, short video script, newsletter, reusable skill), then generate dispatch prompts for the chosen format. It also exposes the format catalog and profile schema as read-only tools, supporting batch routing of an entire content library.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

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.

Enables clients to interact with a bundled documentation corpus through MCP tools and resources over HTTP or stdio, and provides a trajectory evaluation harness for grading agent performance.

Enables reading and extracting text content from PDF files, supporting both local file system access and remote PDF URLs with automatic encoding detection.

Enables reading and verifying C2PA Content Credentials from images and videos through both embedded manifests and invisible watermarks. Supports direct filesystem access, URL checking, and provides structured provenance data including creator identity, AI generation info, and content authenticity.

Enables generation of PDF documents from JSON specs or Markdown, supporting themes, tables, images, and other rich blocks.

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

An MCP server that uses AI to generate structured Asana project plans from diverse sources like text, PDFs, and meeting transcripts. It enables users to preview, generate, and directly sync detailed task lists, subtasks, and dependencies into Asana.

Multi-platform content distribution — draft posts, repurpose content, generate carousels, schedule, analyze performance, create threads. 7 MCP tools.

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 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.

Enables AI agents to securely read and extract information from PDF files including text content, metadata, and page counts from both local files and URLs within the project context.

Local Markdown-to-PDF MCP server with four tools to list themes, render a document, validate an existing PDF, and create a document from a template. Requires Python, Pango/GLib, and fonts; its 0–100 report is heuristic, not print or accessibility certification.

Enables AI assistants to generate professional PDF documents from markdown content with advanced typography, syntax highlighting, math equations, dark mode, and customizable styling options.

MCP server that fetches raw HTML content from a given URL to provide web context to LLMs.

pdf-tools-mcp

A high-performance Model Context Protocol server that enables AI agents to extract text, images, and metadata from PDF documents using parallel processing. It features intelligent Y-coordinate content ordering to preserve natural reading flow and supports both local files and URL-based sources.

A specialized Model Context Protocol server that enhances AI-assisted medical learning by connecting Claude Desktop to PubMed, NCBI Bookshelf, and user documents for searching, retrieving, and analyzing medical education content.