"Extracting Orders from PDF Files" matching MCP servers:

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

MCP server to read protected (or unprotected) PDF files, extracting text to a temp file, and handling large PDFs.

Provides tools for extracting text, performing OCR on scanned documents, and retrieving images from PDF files. It enables Model Context Protocol clients to process PDF content page-by-page through specialized extraction tools.

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

Enables parsing PDF files from local paths or URLs into structured JSON or Markdown format using NetMind's AI-powered PDF extraction service.

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

An MCP (Model Context Protocol) server that provides structured access to ISO 32000 (PDF) specification documents. Enables LLMs to navigate, search, and analyze PDF specifications through well-defined tools.

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

Converts PDF files from local storage or URLs to structured Markdown format using Mistral AI's OCR API, preserving document structure and extracting images.

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.

A Model Context Protocol server that lets AI clients read and process PDF files locally on your Mac without uploading them anywhere, ensuring privacy.

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.

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.

Provides PDF reading, text search, navigation, and form filling capabilities through MCP, with cross-platform PDF viewer integration for seamless document interaction.

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.

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.

Enables reading, searching, and metadata extraction from PDF files without loading the entire content into the context window. It provides efficient tools for text cleaning, page-specific extraction, and context-aware search results.

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

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.

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

Enables reading and extracting content from PDF documents including text (as Markdown), images, tables, and metadata from both local files and URLs, with OCR support for scanned documents.

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 Model Context Protocol server that enables listing and reading PDF documents from a configured directory, extracting text content for MCP clients like Claude Desktop.

Enables AI assistants to inspect, search, compare, and extract text and metadata from PDFs, and to compile Markdown or JSON into styled A4 PDF reports with tables, callouts, code blocks, and images.

Empowers AI agents to securely read and extract information (text, metadata, page count) from PDF files within project contexts using a flexible MCP tool.