"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.

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 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.

Inspects local PDF files, extracting text, counting pages, and performing OCR on scanned PDFs using standard libraries.

Enables PDF file manipulation including merging, splitting, extracting pages, extracting text, excluding pages, and reordering pages.

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.

MCP server for extracting text from PDF files, supporting 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.

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 cryptographic PDF signature verification, tamper detection, PAdES baseline level determination, and PDF/A/PDF/UA conformance validation, along with policy-based trust verdicts.

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.

An MCP server for local PDF inspection that classifies, searches, extracts text and tables without uploading documents or relying on OCR, surfacing reliability warnings for garbled or scanned content.

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 uploading, arranging, merging, and downloading PDFs through an MCP-compatible LLM client by interacting with a PDF Merger API.

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 document processing with Suparse API, extracting structured data from documents into JSON, CSV, XLSX, or Google Sheets, and supports processing single files or folders.

Enables AI agents to permanently remove personally identifiable information from PDF documents, returning redacted files with the sensitive text deleted rather than merely covered. Supports scanned documents, multiple languages, entity types, and optional human review.

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

An MCP (Model Context Protocol) server specialized in deciphering PDF internal structures.

MCP server for generating professional PDFs from structured JSON in AI agents like Claude or Cursor, using pure Node.js with embedded fonts and precision text layout.

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.