{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-microsoft--markitdown","slug":"microsoft--markitdown","name":"markitdown","type":"repo","url":"https://github.com/microsoft/markitdown","page_url":"https://unfragile.ai/microsoft--markitdown","categories":["automation"],"tags":["autogen","autogen-extension","langchain","markdown","microsoft-office","openai","pdf"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-microsoft--markitdown__cap_0","uri":"capability://data.processing.analysis.multi.format.document.to.markdown.conversion.with.structure.preservation","name":"multi-format document-to-markdown conversion with structure preservation","description":"Converts 15+ document formats (DOCX, XLSX, PPTX, PDF, HTML, RSS, MSG, ZIP, EPUB, images, audio) to Markdown by routing each format through a priority-based converter registry that selects the appropriate specialized converter. The system preserves structural semantics (headings, lists, tables, links) rather than extracting raw text, maintaining hierarchical organization and relationships for downstream LLM ingestion and semantic analysis.","intents":["I need to convert a batch of office documents to Markdown for RAG pipeline ingestion","I want to preserve table layouts and heading hierarchies when converting PDFs for LLM analysis","I need to extract structured content from mixed document types while maintaining semantic relationships","I'm building a document understanding pipeline that requires token-efficient Markdown output"],"best_for":["LLM application developers building RAG pipelines","Teams automating document processing for AI ingestion","Developers integrating document conversion into AutoGen or LangChain workflows"],"limitations":["Conversion fidelity depends on source format complexity — complex nested tables or unusual formatting may lose visual styling","External service integrations (Azure Document Intelligence, LLM captioning) add latency and require API credentials","No built-in persistence or caching — each conversion is stateless unless caller implements external state management","Plugin system requires Python knowledge to extend; no low-code extension mechanism"],"requires":["Python 3.9+","python-docx for DOCX conversion","openpyxl for XLSX conversion","python-pptx for PPTX conversion","pdfplumber or pypdf for PDF conversion","requests for web content fetching","Optional: Azure Document Intelligence SDK for advanced PDF/image OCR","Optional: OpenAI/Anthropic API key for image captioning"],"input_types":["file paths (local or remote URIs)","file streams (bytes)","URLs (HTTP/HTTPS)","office documents (DOCX, XLSX, PPTX)","PDFs","web content (HTML, RSS feeds)","images (PNG, JPG, GIF)","audio files","email messages (MSG)","archives (ZIP)","ebooks (EPUB)"],"output_types":["Markdown text","structured Markdown with preserved tables and lists","embedded image references and links"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_1","uri":"capability://tool.use.integration.priority.based.converter.registry.with.dynamic.format.routing","name":"priority-based converter registry with dynamic format routing","description":"Implements a modular converter registry that automatically detects input format (via file extension, MIME type, or stream inspection) and routes to the appropriate specialized converter based on priority rules. The registry supports both built-in converters and dynamically registered plugins, allowing third-party extensions without modifying core code. Format detection uses a fallback chain: explicit format hints → file extension → MIME type → stream content inspection.","intents":["I want to add support for a custom document format without forking the codebase","I need to override the default converter for a format with a custom implementation","I'm processing mixed document types and want automatic format detection","I need to register multiple converters for the same format with different priority levels"],"best_for":["Developers extending MarkItDown with custom converters","Teams with proprietary document formats requiring specialized handling","Organizations building document processing pipelines with format-specific requirements"],"limitations":["Priority-based selection adds ~5-10ms overhead per conversion for registry lookup","Format detection via content inspection is heuristic-based and may fail for ambiguous formats","Plugin registration is runtime-only; no compile-time validation of converter contracts","No built-in versioning or compatibility checking for plugins"],"requires":["Python 3.9+","Understanding of DocumentConverter interface contract","For plugins: ability to implement convert(uri, **kwargs) -> DocumentConverterResult"],"input_types":["file paths","file streams","URIs","format hints (explicit type specification)"],"output_types":["converter instance selection","routing decision metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_10","uri":"capability://tool.use.integration.plugin.system.with.documentconverter.interface.contract","name":"plugin system with documentconverter interface contract","description":"Provides an extensible plugin architecture where third-party converters implement the DocumentConverter interface (convert(uri, **kwargs) -> DocumentConverterResult) and register with the converter registry. Plugins are discovered and loaded at runtime, allowing custom format support without modifying core code. The system validates plugin contracts and handles registration priority for format conflicts.","intents":["I need to add support for a proprietary document format","I want to override the default converter for a format with custom logic","I'm building a document processing platform and need extensibility","I need to register multiple converters for the same format with different priorities"],"best_for":["Developers extending MarkItDown with custom converters","Teams with proprietary document formats","Organizations building document processing platforms"],"limitations":["Plugin registration is runtime-only; no compile-time validation","No built-in versioning or compatibility checking for plugins","Plugin discovery requires explicit registration; no automatic scanning","No sandboxing; plugins have full access to system resources","Limited documentation on plugin development patterns"],"requires":["Python 3.9+","Understanding of DocumentConverter interface","Ability to implement convert(uri, **kwargs) -> DocumentConverterResult","Knowledge of MarkItDown's converter registry API"],"input_types":["custom document formats","proprietary file types"],"output_types":["Markdown output via DocumentConverterResult"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_11","uri":"capability://tool.use.integration.mcp.server.integration.for.ai.assistant.compatibility","name":"mcp server integration for ai assistant compatibility","description":"Exposes MarkItDown as a Model Context Protocol (MCP) server, enabling integration with AI assistants (Claude Desktop, etc.) that support MCP. The server implements MCP resource and tool interfaces, allowing assistants to invoke document conversion as a native capability. This enables AI assistants to convert documents on behalf of users without leaving the chat interface.","intents":["I want to use Claude Desktop to convert documents to Markdown","I need to give an AI assistant the ability to process documents","I'm building an AI agent that needs document conversion capabilities","I want to integrate document conversion into an MCP-compatible AI workflow"],"best_for":["AI assistant users wanting document conversion in chat","Developers building MCP-compatible AI agents","Teams integrating document processing into AI workflows"],"limitations":["Requires MCP-compatible AI assistant (Claude Desktop, etc.)","MCP server adds network latency for remote document processing","Large documents may exceed MCP message size limits","No built-in authentication; relies on MCP server security model","Requires separate MCP server deployment or local process"],"requires":["Python 3.9+","markitdown-mcp package","MCP-compatible AI assistant (Claude Desktop, etc.)","Network connectivity to MCP server"],"input_types":["document URIs passed via MCP protocol","file paths accessible to MCP server"],"output_types":["Markdown content returned via MCP protocol","MCP resource references"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_12","uri":"capability://automation.workflow.command.line.interface.with.batch.processing.and.streaming","name":"command-line interface with batch processing and streaming","description":"Provides a CLI entry point (markitdown command) for batch processing documents from the shell. Supports reading from file paths, URLs, or stdin, and outputs Markdown to stdout or files. The CLI integrates with shell pipelines, enabling document conversion as part of larger automation workflows. Supports configuration via command-line flags and environment variables.","intents":["I need to convert documents from the command line for shell scripts","I want to process a batch of documents in a pipeline","I'm automating document conversion as part of a larger workflow","I need to convert documents without writing Python code"],"best_for":["DevOps engineers automating document processing","System administrators building document pipelines","Users preferring command-line interfaces"],"limitations":["CLI is synchronous; no built-in parallelization for batch processing","Large files may cause memory issues when reading into memory","No progress reporting for long-running conversions","Limited error handling and recovery options","Configuration via environment variables may be fragile"],"requires":["Python 3.9+","MarkItDown installed and in PATH","Shell environment (bash, zsh, etc.)"],"input_types":["file paths","URLs","stdin"],"output_types":["stdout","files"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_13","uri":"capability://tool.use.integration.python.api.with.programmatic.integration.and.custom.workflows","name":"python api with programmatic integration and custom workflows","description":"Exposes MarkItDown as a Python library via the MarkItDown class, enabling programmatic integration into Python applications, LangChain agents, and AutoGen workflows. The API accepts file paths, streams, or URIs and returns DocumentConverterResult objects containing Markdown content and metadata. Supports custom configuration, error handling, and integration with Python-based document processing pipelines.","intents":["I need to integrate document conversion into a Python application","I want to use MarkItDown in a LangChain or AutoGen workflow","I'm building a document processing pipeline in Python","I need to convert documents programmatically with custom error handling"],"best_for":["Python developers building LLM applications","Teams using LangChain or AutoGen frameworks","Organizations building document processing pipelines"],"limitations":["Requires Python knowledge; not suitable for non-technical users","No async support; conversions are synchronous and blocking","Large documents may cause memory issues","Error handling requires explicit try-catch blocks","No built-in caching or result persistence"],"requires":["Python 3.9+","markitdown package installed","Python development environment"],"input_types":["file paths","file streams","URIs"],"output_types":["DocumentConverterResult objects","Markdown strings","Metadata dictionaries"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_14","uri":"capability://data.processing.analysis.uri.handling.with.automatic.format.detection.and.stream.resolution","name":"uri handling with automatic format detection and stream resolution","description":"Handles various input URI formats (file paths, HTTP/HTTPS URLs, file:// URIs) with automatic format detection based on file extension, MIME type, or content inspection. The system resolves URIs to streams, handles redirects and authentication where applicable, and routes to the appropriate converter. Supports both local and remote document sources transparently.","intents":["I need to convert documents from URLs without downloading manually","I want to process both local files and remote documents with the same API","I need automatic format detection for mixed input sources","I'm building a pipeline that accepts documents from various sources"],"best_for":["Developers building document processing pipelines","Teams processing documents from mixed sources","LLM application developers ingesting remote documents"],"limitations":["Remote document fetching requires network connectivity","Large remote files may timeout or exceed memory limits","Authentication is not supported for protected URLs","Redirects are followed automatically but may cause issues with some services","Format detection via content inspection is heuristic-based"],"requires":["Python 3.9+","requests library for HTTP fetching","Network connectivity for remote URIs"],"input_types":["file paths (local)","HTTP/HTTPS URLs","file:// URIs","file streams"],"output_types":["resolved streams","format detection metadata"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_15","uri":"capability://automation.workflow.exception.handling.with.detailed.error.context.and.recovery.suggestions","name":"exception handling with detailed error context and recovery suggestions","description":"Implements structured exception handling that captures conversion errors with detailed context (file type, converter used, error location) and provides recovery suggestions. The system distinguishes between recoverable errors (format not supported, missing optional dependency) and fatal errors (corrupted file, network timeout). Error messages include actionable guidance for users.","intents":["I need to understand why a document conversion failed","I want to implement error recovery in my conversion pipeline","I need to distinguish between temporary and permanent conversion failures","I want detailed error messages for debugging conversion issues"],"best_for":["Developers building robust document processing pipelines","Teams implementing error recovery and retry logic","Organizations requiring detailed conversion diagnostics"],"limitations":["Error context may be verbose for complex failures","Recovery suggestions are generic; domain-specific guidance requires custom handling","Some errors may not be caught until document processing begins","Error messages depend on underlying converter implementations"],"requires":["Python 3.9+","Understanding of exception handling patterns"],"input_types":["conversion errors from any converter"],"output_types":["structured exception objects with context","error messages with recovery suggestions"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_16","uri":"capability://automation.workflow.docker.deployment.with.containerized.conversion.service","name":"docker deployment with containerized conversion service","description":"Provides Docker configuration for deploying MarkItDown as a containerized service, enabling scalable document conversion infrastructure. The Docker image includes all dependencies and optional services (Azure Document Intelligence, LLM APIs), allowing deployment to container orchestration platforms (Kubernetes, Docker Compose). Supports environment variable configuration for API credentials and service endpoints.","intents":["I need to deploy MarkItDown as a scalable service","I want to containerize document conversion for cloud deployment","I'm building a microservice architecture with document processing","I need to run MarkItDown in a Kubernetes cluster"],"best_for":["DevOps engineers deploying document processing services","Organizations building microservice architectures","Teams requiring scalable document conversion infrastructure"],"limitations":["Docker image size may be large due to dependencies","Container startup time may be slow for large dependency sets","Persistent storage requires external volume configuration","API credentials must be passed via environment variables (security consideration)","No built-in load balancing or auto-scaling"],"requires":["Docker installed and running","Docker Compose or Kubernetes for orchestration","Environment variables for API credentials"],"input_types":["HTTP requests to containerized service","mounted volumes with documents"],"output_types":["HTTP responses with Markdown content","files written to mounted volumes"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_2","uri":"capability://data.processing.analysis.office.document.structure.extraction.with.semantic.preservation","name":"office document structure extraction with semantic preservation","description":"Extracts content from DOCX, XLSX, and PPTX files using python-docx, openpyxl, and python-pptx libraries respectively, preserving document structure (headings, lists, tables, text formatting) as Markdown semantic elements. The converters parse the underlying XML structure of Office Open XML format to reconstruct hierarchical organization, maintaining heading levels, list nesting, table layouts, and hyperlinks in Markdown syntax.","intents":["I need to extract a Word document's heading hierarchy and convert to Markdown outline","I want to preserve Excel table structure when converting spreadsheets for LLM analysis","I need to convert PowerPoint slides to Markdown while maintaining slide structure and speaker notes","I'm processing mixed Office documents and need consistent semantic structure in output"],"best_for":["Enterprise document processing pipelines using Microsoft Office formats","Teams migrating Office documents to Markdown-based knowledge bases","LLM application developers ingesting corporate documents"],"limitations":["Complex formatting (columns, text boxes, embedded shapes) is simplified to plain text","Embedded objects (OLE, ActiveX) are skipped; only extractable text is converted","Macro-generated content is not executed; only static content is extracted","XLSX conversion flattens multi-sheet workbooks into sequential tables without sheet metadata","PPTX speaker notes are extracted but slide animations and transitions are discarded"],"requires":["Python 3.9+","python-docx library","openpyxl library","python-pptx library"],"input_types":["DOCX files (Word documents)","XLSX files (Excel spreadsheets)","PPTX files (PowerPoint presentations)"],"output_types":["Markdown with preserved heading hierarchy","Markdown tables","Markdown lists with nesting","Hyperlinks in Markdown syntax"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_3","uri":"capability://data.processing.analysis.pdf.content.extraction.with.optional.ocr.via.azure.document.intelligence","name":"pdf content extraction with optional ocr via azure document intelligence","description":"Extracts text and structure from PDFs using pdfplumber (text-based extraction) with optional integration to Azure Document Intelligence for advanced OCR, layout analysis, and table detection. The system detects whether a PDF is text-based or scanned and routes to the appropriate extraction method. Azure integration enables extraction of text from image-heavy PDFs and detection of complex table structures that text-only extraction would miss.","intents":["I need to extract text from a scanned PDF that pdfplumber can't handle","I want to preserve table structure from a complex PDF layout","I'm processing mixed text-based and scanned PDFs and need automatic method selection","I need to extract text from image-heavy PDFs with OCR"],"best_for":["Organizations processing scanned documents and image-heavy PDFs","Teams requiring high-fidelity table extraction from complex layouts","Enterprise document pipelines with budget for Azure services"],"limitations":["Text-only extraction (pdfplumber) fails on scanned PDFs without OCR","Azure Document Intelligence adds 2-5 second latency per document and requires API calls","Azure integration requires valid credentials and incurs per-page costs","Complex PDF layouts (multi-column, rotated text) may produce suboptimal Markdown","Embedded fonts and special characters may not extract correctly"],"requires":["Python 3.9+","pdfplumber library for text extraction","Optional: Azure Document Intelligence SDK and valid Azure credentials for OCR","Optional: Azure subscription with Document Intelligence resource provisioned"],"input_types":["PDF files (text-based or scanned)","PDF streams"],"output_types":["Markdown text","Markdown tables","OCR-extracted text with layout preservation"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_4","uri":"capability://data.processing.analysis.web.content.extraction.with.rss.and.youtube.support","name":"web content extraction with rss and youtube support","description":"Extracts content from web pages (HTML), RSS feeds, and YouTube videos by fetching remote content via HTTP requests and parsing with BeautifulSoup (HTML) or specialized feed parsers (RSS). The system handles URL resolution, follows redirects, extracts main content while filtering navigation/ads, and converts to Markdown. YouTube integration extracts video metadata and transcripts when available.","intents":["I need to convert a web page to Markdown for LLM analysis","I want to extract articles from an RSS feed and convert to Markdown","I need to extract YouTube video transcripts and metadata","I'm building a web scraping pipeline that outputs Markdown"],"best_for":["Developers building web content ingestion pipelines for RAG","Teams automating content extraction from news feeds and blogs","LLM application developers processing web-based knowledge sources"],"limitations":["Requires network access; cannot process offline content","JavaScript-rendered content is not executed; only static HTML is extracted","Some websites block automated requests or require authentication","RSS feed parsing depends on feed validity; malformed feeds may fail","YouTube transcript extraction requires video to have captions enabled","Large web pages may timeout or exceed memory limits"],"requires":["Python 3.9+","requests library for HTTP fetching","beautifulsoup4 for HTML parsing","feedparser for RSS parsing","Network connectivity","Optional: YouTube API key for enhanced transcript extraction"],"input_types":["HTTP/HTTPS URLs","RSS feed URLs","YouTube video URLs"],"output_types":["Markdown text","Extracted article content","Video metadata and transcripts"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_5","uri":"capability://image.visual.image.analysis.with.llm.powered.captioning.and.optional.ocr","name":"image analysis with llm-powered captioning and optional ocr","description":"Processes image files (PNG, JPG, GIF) by either extracting embedded text via OCR or generating descriptive captions using LLM APIs (OpenAI, Anthropic). The system detects image type, optionally calls Azure Document Intelligence for text extraction, and falls back to LLM captioning for visual description. Output includes extracted text and/or generated captions in Markdown format.","intents":["I need to extract text from images for document processing","I want to generate descriptions of images for LLM context","I'm processing mixed documents with embedded images and need automatic handling","I need to convert image-heavy documents to Markdown with visual descriptions"],"best_for":["Teams processing documents with embedded images","LLM application developers needing image understanding in text pipelines","Organizations with scanned documents containing images"],"limitations":["LLM captioning adds 1-3 second latency per image and requires API calls","LLM captioning incurs per-image costs (varies by provider)","OCR accuracy depends on image quality and text clarity","Generated captions may be verbose or miss domain-specific details","No support for animated GIFs; only first frame is processed"],"requires":["Python 3.9+","Pillow library for image handling","Optional: Azure Document Intelligence SDK for OCR","Optional: OpenAI or Anthropic API key for LLM captioning","Optional: Azure subscription for Document Intelligence"],"input_types":["PNG files","JPG/JPEG files","GIF files","image streams"],"output_types":["Markdown with extracted text","Markdown with LLM-generated captions","Combined text and caption output"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_6","uri":"capability://data.processing.analysis.audio.file.metadata.extraction.and.optional.transcription","name":"audio file metadata extraction and optional transcription","description":"Extracts metadata from audio files (MP3, WAV, FLAC, etc.) including title, artist, duration, and bitrate using audio metadata libraries. Optionally integrates with speech-to-text services (Azure Speech, OpenAI Whisper) to generate transcripts. Output includes metadata and transcripts in Markdown format suitable for LLM ingestion.","intents":["I need to extract metadata from audio files for cataloging","I want to transcribe audio files to Markdown for LLM analysis","I'm processing mixed media documents and need automatic audio handling","I need to convert podcasts or recordings to searchable text"],"best_for":["Teams processing multimedia documents","Organizations managing audio archives","LLM application developers needing audio understanding"],"limitations":["Transcription adds 5-30 second latency depending on audio length and service","Transcription requires external API calls and incurs per-minute costs","Transcription accuracy depends on audio quality and language","Large audio files may exceed service limits or timeout","No speaker diarization or emotion detection in base implementation"],"requires":["Python 3.9+","mutagen or similar library for metadata extraction","Optional: Azure Speech SDK or OpenAI Whisper API for transcription","Optional: API credentials for transcription service"],"input_types":["MP3 files","WAV files","FLAC files","audio streams"],"output_types":["Markdown with audio metadata","Markdown with transcripts","Combined metadata and transcript output"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_7","uri":"capability://data.processing.analysis.email.message.extraction.with.attachment.handling","name":"email message extraction with attachment handling","description":"Extracts content from email message files (MSG format) including headers (from, to, subject, date), body text, and metadata. Recursively processes attachments by routing them through the converter registry, allowing embedded documents to be converted to Markdown. Output includes email metadata and converted attachment content in Markdown format.","intents":["I need to extract email content and convert to Markdown for archival","I want to process email attachments automatically as part of document conversion","I'm building an email-to-knowledge-base pipeline","I need to extract email threads with attachments for LLM analysis"],"best_for":["Organizations archiving email to Markdown-based systems","Teams automating email document processing","LLM application developers ingesting email-based knowledge"],"limitations":["Only MSG format supported; Outlook PST/OST files require separate handling","HTML email bodies may contain complex formatting that doesn't convert cleanly","Embedded images in email are extracted but may lose context","Large attachments may cause memory issues or timeout","Email threading and conversation context is not preserved"],"requires":["Python 3.9+","python-pptx or similar library for MSG parsing","Recursive access to converter registry for attachment processing"],"input_types":["MSG files (Outlook email messages)"],"output_types":["Markdown with email metadata","Converted attachment content","Combined email and attachment output"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_8","uri":"capability://data.processing.analysis.archive.extraction.with.recursive.format.conversion","name":"archive extraction with recursive format conversion","description":"Extracts and processes files from ZIP archives by unpacking contents and routing each file through the converter registry based on detected format. Supports nested archives and mixed file types within a single ZIP. Output includes converted content from all archive members in Markdown format, maintaining file organization metadata.","intents":["I need to process a ZIP archive containing mixed document types","I want to convert all documents in an archive to Markdown in one operation","I'm processing nested archives with multiple file types","I need to extract and convert archive contents for LLM ingestion"],"best_for":["Teams processing bulk document archives","Organizations automating archive-to-Markdown conversion","LLM application developers ingesting archived documents"],"limitations":["Nested archives are flattened; directory structure is not preserved","Large archives may exceed memory limits during extraction","Password-protected archives are not supported","Binary files without recognized converters are skipped","Archive member ordering is not guaranteed"],"requires":["Python 3.9+","zipfile library (standard library)","Access to full converter registry for recursive format routing"],"input_types":["ZIP files","nested ZIP archives"],"output_types":["Markdown with converted archive contents","File organization metadata","Combined output from all archive members"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-microsoft--markitdown__cap_9","uri":"capability://data.processing.analysis.epub.ebook.extraction.with.chapter.and.metadata.preservation","name":"epub ebook extraction with chapter and metadata preservation","description":"Extracts content from EPUB ebook files by parsing the underlying ZIP structure and XML metadata, preserving chapter organization, headings, and metadata (title, author, publication date). Converts EPUB's XHTML content to Markdown while maintaining reading order and structural hierarchy. Output includes ebook metadata and chapter-organized Markdown content.","intents":["I need to convert an EPUB ebook to Markdown for LLM analysis","I want to extract ebook metadata and chapter structure","I'm processing mixed document types including ebooks","I need to convert ebooks to searchable Markdown format"],"best_for":["Organizations digitizing ebook collections","Teams processing mixed document types including ebooks","LLM application developers ingesting ebook content"],"limitations":["Complex EPUB layouts (multi-column, sidebars) are simplified to linear Markdown","Embedded fonts and styling are not preserved","DRM-protected EPUBs cannot be processed","Images within EPUBs are extracted but may lose context","EPUB3 features (audio, video) are not supported"],"requires":["Python 3.9+","ebooklib or similar EPUB parsing library","XML parsing capabilities"],"input_types":["EPUB files (ebooks)"],"output_types":["Markdown with chapter structure","Ebook metadata","Chapter-organized content"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":54,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","python-docx for DOCX conversion","openpyxl for XLSX conversion","python-pptx for PPTX conversion","pdfplumber or pypdf for PDF conversion","requests for web content fetching","Optional: Azure Document Intelligence SDK for advanced PDF/image OCR","Optional: OpenAI/Anthropic API key for image captioning","Understanding of DocumentConverter interface contract","For plugins: ability to implement convert(uri, **kwargs) -> DocumentConverterResult"],"failure_modes":["Conversion fidelity depends on source format complexity — complex nested tables or unusual formatting may lose visual styling","External service integrations (Azure Document Intelligence, LLM captioning) add latency and require API credentials","No built-in persistence or caching — each conversion is stateless unless caller implements external state management","Plugin system requires Python knowledge to extend; no low-code extension mechanism","Priority-based selection adds ~5-10ms overhead per conversion for registry lookup","Format detection via content inspection is heuristic-based and may fail for ambiguous formats","Plugin registration is runtime-only; no compile-time validation of converter contracts","No built-in versioning or compatibility checking for plugins","Plugin registration is runtime-only; no compile-time validation","Plugin discovery requires explicit registration; no automatic scanning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9038915067442062,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:24.501Z","last_commit":"2026-04-20T17:52:20Z"},"community":{"stars":119756,"forks":7959,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=microsoft--markitdown","compare_url":"https://unfragile.ai/compare?artifact=microsoft--markitdown"}},"signature":"lCiipka5R08J1Ii8kOLN5xfKuevTPQ9WGpKiAsjrBK/AnYlffretrPEDoHXadqQ96t5R6AwvMpALqmfu6C/fAQ==","signedAt":"2026-06-22T19:25:12.902Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/microsoft--markitdown","artifact":"https://unfragile.ai/microsoft--markitdown","verify":"https://unfragile.ai/api/v1/verify?slug=microsoft--markitdown","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}