"Extracting Orders from PDF Files" matching MCP servers:

Provides tools for reading and extracting text from PDF files, supporting both local files and URLs.

MCP server for extracting text from PDF files, supporting local files and URLs.

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

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

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

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

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.

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.

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.

A Model Context Protocol server that enables AI assistants to read PDF files, extract text, retrieve metadata, read specific pages, and search within PDFs.

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 bidirectional conversion between Markdown and PDF formats, including text extraction from specific pages and metadata retrieval. It supports customizable PDF output sizes and document processing through standard MCP tools.

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.

Enables AI assistants to read, create, merge, split, and edit PDF files through natural language commands in GitHub Copilot Chat.

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)

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.