Herramientas

Extracts text and tables from PDFs; merges, splits and rotates pages, adds watermarks, fills forms and OCRs scanned documents. The official Anthropic skill behind Claude's document features; source-available, not open source.

Adds a searchable, copyable text layer to scanned PDFs, with automatic deskew, rotation and size optimization, outputting PDF/A by default. Built on Tesseract, supports 100+ languages and runs fully locally.

Scans files and directories for personal data such as names, emails and credit card numbers using Presidio Analyzer, with configurable entity types, confidence thresholds and ignore rules. Needs Python 3.10+ and a spaCy language model.

Lightweight OCR built on PaddleOCR models converted to ONNX, recognizing Chinese and English by default on CPU alone. The rapidocr CLI reads text from screenshots, photos and scanned page images. Needs Python; downloads models on first run, then works offline. Convert PDFs to images first.

Converts PDFs to editable Word files, keeping paragraphs, tables, images and layout as closely as possible, and can extract tables alone, handy when a contract or exam only exists as PDF. Needs Python; digital PDFs only, OCR scans first. No longer actively maintained by Artifex.

Scans codebases and dependencies for open-source licenses, copyright notices and package manifests, detecting licenses by full-text matching, with output in JSON, SPDX, CycloneDX and more, for open-source compliance reviews.

From Free Law Project: finds bad redactions in PDFs, where a black box covers text that can still be selected and copied, and outputs the leaked text and positions per page as JSON. Useful for lawyers checking filings before release. Needs Python 3.10+; may miss cases, so review important files.

The Docling project's official MCP: converts PDF, Word, PowerPoint and more into structured documents with layout and table detection and OCR for scans, and lets agents build new documents paragraph by paragraph and export Markdown. Local mode needs Python and uv, or use your own Docling Serve.

Local MCP for paralegal work: analyzes clause risk in Word contracts, compares two contract versions, extracts parties, dates and other key terms, outlines pleadings, and optionally searches US case law via CourtListener. US-focused; needs local Python. A lawyer must review results.