"Tools and methods for extracting text from PDF files" matching MCP servers:

Enables structured extraction of methods and reproducibility heuristics from academic papers, allowing AI agents to obtain metadata, full text, structured methods, code repository discovery, and a no-clone reproducibility verdict from a paper URL.

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 perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

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.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

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

Enables extracting visible text and links from webpages for content analysis.

Full-stack PDF intelligence MCP server for extracting, manipulating, annotating, converting, validating, and RAG-searching PDFs through a unified tool surface and React workbench.

A lightweight MCP server for basic file operations, enabling reading, writing, and listing files securely via the Model Context Protocol.

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.

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.

Turn markdown into designed PDFs with cover page, table of contents, and code blocks that hold across pages. One command from Claude Desktop, Claude Code, Cursor, Cline, Zed, or any MCP-capable client.

Extracts images from PDF files, allowing LLMs to access and analyze embedded images from local PDF documents.

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

Converts Markdown files to Word (.docx) and PDF documents. Supports .dotx templates for branded styling and batch conversion.

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.

A line-oriented text file editor. Optimized for LLM tools with efficient partial file access to minimize token usage.

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