Tools

From Microsoft: converts PDF, Word, Excel, PowerPoint, HTML and other files or URLs to Markdown so agents can read and search them. A single convert_to_markdown tool over STDIO or local HTTP.

Converts PDF, Word, Excel, PowerPoint, HTML, images, audio and more into Markdown that keeps headings, lists and tables, ready for LLMs to read and analyze. Maintained by Microsoft; requires Python 3.10+.

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.

Official PaddleOCR MCP: text recognition and layout parsing for images and PDFs (PP-OCR, PP-StructureV3, PaddleOCR-VL), returning text or Markdown. Runs inference locally, or calls the official API or a self-hosted service.

Parses PDF, DOCX, PPTX, XLSX, HTML, images and audio, recovering layout, reading order, tables and formulas, and exports Markdown, HTML or lossless JSON. Started by IBM Research, now hosted by the LF AI & Data Foundation; runs fully locally.

MuseScore 4 command-line wrapper from CLI-Anything: lets agents transpose scores, extract parts, add or remove instruments, inspect score info and export PDF, MIDI, MusicXML, MP3 or braille. Handy for music teachers preparing scores in bulk. Needs MuseScore 4 and Python 3.10+.

Converts PDFs, images, PPTX, DOCX, XLSX, EPUB and more into Markdown, JSON or HTML, preserving tables, formulas and layout, with optional LLM help for higher accuracy. Needs Python 3.10+ and PyTorch; large-scale commercial use of the model weights needs a paid license.

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.

Distills technical books, doc folders or paper collections (PDF, EPUB, DOCX, Markdown and more) into an agent skill with a core framework, on-demand chapter files, glossary and cheat sheet for lookup while working. Extraction needs local Python; poppler or docling optional for PDFs.

Write your resume in YAML and render a cleanly typeset PDF from the command line: multiple themes, fine control of margins, colors and fonts, strict field validation and multi-language support. A companion agent skill is available. Needs Python 3.12+.

Reads the exact position of every character, line and table cell in digital PDFs: the CLI exports layout-preserving text or CSV/JSON details, and the Python API extracts tables and form fields, ideal for invoices, statements and reports. Runs locally with Python; scans need OCR first.

PDF translation CLI for research papers that preserves formulas and layout as much as possible, producing bilingual side-by-side or translation-only PDFs. Needs an OpenAI-compatible API (local Ollama works too); currently focused on English to Chinese.

Rendering engine that lays out HTML and CSS and exports PDF, supporting paged media and other print style standards, ideal for generating reports, invoices and receipts in bulk. Written in Python; depends on the system Pango library.

Open-source PDF table extraction with a camelot CLI: exports tables from financial reports, bank statements and statistical reports to Excel, CSV or Markdown, with an accuracy score. Needs Python 3.10+; handles text-based PDFs by default, scans need the ml and ocr extras. Double-check results.

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.

Recognizes images or whole PDFs (including scans) with math formulas and tables into Markdown and LaTeX, strong on English and Chinese and supporting 80+ languages, handy for digitizing exams, lecture notes and study notes. An open-source Mathpix alternative that runs locally with Python.

Unified search across 20+ academic sources including arXiv, PubMed, bioRxiv, Semantic Scholar and OpenAlex, returning deduplicated, normalized results and downloading full text via open-access channels first. Most sources need no API key.

Extracts issuer, amount, date, invoice number and other fields from PDF invoices using YAML/JSON templates, exporting CSV, JSON or XML. Ships several text-extraction backends, OCR for scanned invoices and an optional AI fallback.

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.

Python library with a CLI for Factur-X (ZUGFeRD 2.x), the French-German e-invoicing standard: combine a regular PDF invoice and XML into a Factur-X invoice, extract the XML from invoice PDFs and validate it against the official XSD; also handles Order-X and UBL. Needs Python 3.

MinerU's official MCP: parses PDF, Word, PowerPoint and images into Markdown via the mineru.net cloud API, keeping tables and formulas. The keyless Flash mode works out of the box; add a token for higher quotas and more formats.