"Extracting Orders from PDF Files" matching MCP servers:

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

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

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 AI agents to read, search, and extract content from PDF files.

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.

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

An MCP server that provides a tool to extract text content from local PDF files, supporting both standard PDF reading and OCR capabilities with optional page selection.

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

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

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.

A PDF processing server that extracts text via normal parsing or OCR, and retrieves images from PDF files through the MCP protocol with a built-in web debugger.

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

Enables AI apps to extract tables from local text PDFs and return each table as CSV text, optionally saving the CSV files to a specified output directory.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Analyzes Python project structure by exploring directory trees, extracting outlines from Python and Markdown files, and inspecting OpenAPI specifications.

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

Enables AI assistants to inspect generated .docx and .pdf files before delivery, extracting structural, typographic, spacing, and language facts so formatting errors can be caught automatically.

Enables read-only scanning and text extraction from PDF, DOCX, PPTX, SVG, and PNG files in a local folder, providing the raw text to AI models for summarization or analysis without an external LLM API.

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

An MCP server that extracts energy-usage charts from utility-bill PDFs, returning structured time-series data and annotated images.

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

Enables AI agents to read and extract text, tables, and image content from files (PDF, DOCX, PPTX, SVG, PNG) within a folder, providing structured data for interpretation by the calling model.

Enables load testing and validation of an order API by generating synthetic orders, previewing them, and sending batches while enforcing guardrails like bounded counts and fixed seeds for replay.

Provides AI agents with persistent long-term memory over MCP, extracting durable facts from conversations, storing them as plain files, and injecting ranked memories into later sessions via recall and search tools.

MCP server for parsing .eml email files, extracting metadata, content, and attachments with smart organization into folders. Enables AI to read and handle email files offline without triggering trackers.

A Model Context Protocol (MCP) server that provides comprehensive PDF redaction capabilities, including text and image redaction, search, and session-based in-memory operations using FastMCP and pymupdf.

MCP server to batch download open-access PDFs from PubMed Central (PMC), supporting PMCID/PMID lookup, parallel downloads, and download reports.

Removes the 'Made with Gamma' watermark from PDF and PowerPoint files exported from Gamma.app using structural and lossless object deletion.

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.

Enables searching Telegram messages and chats, retrieving recent messages, and extracting text from attached documents (PDF, TXT, MD, CSV) using your personal Telegram account.