{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-zcaceres--markdownify-mcp","slug":"zcaceres--markdownify-mcp","name":"markdownify-mcp","type":"mcp","url":"https://github.com/zcaceres/markdownify-mcp","page_url":"https://unfragile.ai/zcaceres--markdownify-mcp","categories":["mcp-servers"],"tags":["ai","anthropic","anthropic-ai","anthropic-claude","markdown","mcp","model-context-protocol","ocr","tools"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-zcaceres--markdownify-mcp__cap_0","uri":"capability://tool.use.integration.mcp.based.tool.registration.and.request.routing","name":"mcp-based tool registration and request routing","description":"Implements a Model Context Protocol server that registers conversion tools as callable endpoints and routes incoming tool-call requests to appropriate handlers. The server uses TypeScript/Node.js to expose a standardized MCP interface that clients can discover via list-tools and invoke via call-tool, with Zod schema validation for all input parameters before routing to the Markdownify core engine.","intents":["Integrate Markdownify into Claude Desktop or other MCP-compatible clients without custom API wrappers","Expose multiple conversion tools through a single standardized protocol endpoint","Validate and safely route conversion requests with schema-based parameter checking"],"best_for":["AI application developers building MCP-compatible integrations","Teams deploying Markdownify as a shared service for Claude Desktop or other MCP clients"],"limitations":["MCP protocol overhead adds ~50-100ms per request compared to direct function calls","Requires MCP-compatible client; cannot be used with REST-only applications without additional adapter","Tool discovery is static at server startup; dynamic tool registration not supported"],"requires":["Node.js 18+","TypeScript runtime or compiled JavaScript","MCP-compatible client application (Claude Desktop, custom MCP client, etc.)"],"input_types":["JSON-serialized tool parameters","URL strings","File paths"],"output_types":["JSON-serialized tool results","Markdown text content"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_1","uri":"capability://data.processing.analysis.pdf.document.to.markdown.conversion","name":"pdf document to markdown conversion","description":"Converts PDF files to Markdown by delegating to the Python markitdown library, which extracts text, tables, and structural metadata from PDF documents and formats them as semantic Markdown. Handles both local file paths and remote URLs, manages temporary file storage for URL-sourced PDFs, and preserves document structure including headings, lists, and table formatting.","intents":["Convert research papers or technical PDFs into searchable, LLM-friendly Markdown format","Extract structured content from PDF reports while preserving table layouts and hierarchical organization","Batch process PDF archives into Markdown for knowledge base ingestion"],"best_for":["Researchers and knowledge workers processing academic or technical PDFs","Teams building RAG systems that need to ingest PDF documents","Developers automating document pipeline workflows"],"limitations":["Complex layouts with multi-column text may not preserve spatial relationships in Markdown","Scanned PDFs without OCR will produce empty or minimal output; OCR not built-in","Large PDFs (>100MB) may cause memory pressure in the Node.js process managing temp files","Embedded images in PDFs are extracted but not embedded in output Markdown; only text content is preserved"],"requires":["Python 3.8+ with markitdown package installed via uv","PDF file accessible via local path or HTTP(S) URL","Temporary directory writable by Node.js process for URL-sourced PDFs"],"input_types":["application/pdf","file path (local)","URL (http/https)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","document-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_10","uri":"capability://tool.use.integration.python.subprocess.execution.with.uv.package.manager","name":"python subprocess execution with uv package manager","description":"Executes the Python markitdown tool as a subprocess, managing the Python environment through the uv package manager for dependency isolation and reproducible builds. The Markdownify class spawns the markitdown process with input file path and captures stdout/stderr, handling subprocess lifecycle, error codes, and output parsing without requiring system-wide Python installation.","intents":["Execute Python-based conversion logic from Node.js without direct Python integration","Maintain isolated Python environment with uv for reproducible deployments","Handle subprocess errors and timeouts gracefully"],"best_for":["Teams deploying Markdownify in containerized or isolated environments","Systems requiring reproducible Python dependency versions","Developers avoiding direct Python/Node.js FFI complexity"],"limitations":["Subprocess overhead adds ~100-500ms per conversion compared to direct Python library calls","Large output (>100MB Markdown) may cause memory pressure when buffering stdout","No streaming output; entire result must be buffered before returning to client","Subprocess crashes or hangs require timeout handling; no built-in timeout mechanism","uv installation and Python environment setup required; adds deployment complexity"],"requires":["Python 3.8+ installed on system","uv package manager installed and in PATH","markitdown Python package installed via uv","Node.js child_process module available"],"input_types":["file path (local)","command-line arguments"],"output_types":["stdout (Markdown text)","stderr (error messages)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_11","uri":"capability://safety.moderation.zod.schema.validation.for.tool.parameters","name":"zod schema validation for tool parameters","description":"Validates all tool parameters using Zod schemas before passing to conversion handlers, ensuring type safety and preventing invalid inputs from reaching the Python subprocess. The MCP server layer defines schemas for each tool (e.g., URL format, file path existence) and validates incoming requests, returning detailed error messages for validation failures without executing conversions.","intents":["Prevent invalid inputs from reaching conversion logic and causing subprocess errors","Provide clear error messages to clients when parameters are malformed","Enforce consistent parameter validation across all conversion tools"],"best_for":["Systems requiring strict input validation before expensive conversions","Teams building robust MCP servers with clear error contracts","Applications needing detailed validation error messages for debugging"],"limitations":["Validation adds ~10-50ms latency per request","Schemas must be manually maintained; no automatic schema generation from Python code","Complex validation rules (e.g., file existence checks) may require custom validators","Validation errors are returned to client but don't prevent logging or monitoring"],"requires":["Zod library installed (npm dependency)","Schema definitions for each tool parameter"],"input_types":["JSON-serialized parameters"],"output_types":["validation result (pass/fail with error details)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_2","uri":"capability://data.processing.analysis.docx.xlsx.pptx.office.document.conversion","name":"docx/xlsx/pptx office document conversion","description":"Converts Microsoft Office formats (Word, Excel, PowerPoint) to Markdown by delegating to markitdown's Python handlers, which parse the Office Open XML structure and extract text, tables, slides, and formatting metadata. Supports both local files and remote URLs, with temporary file management for URL sources and preservation of document structure including nested tables and multi-slide presentations.","intents":["Convert business reports and presentations into Markdown for collaborative editing or LLM processing","Extract tabular data from Excel spreadsheets into Markdown table format","Transform PowerPoint slides into Markdown outline format for content repurposing"],"best_for":["Business teams migrating Office documents to Markdown-based workflows","Data analysts extracting structured data from Excel files","Content creators converting presentations into written documentation"],"limitations":["Complex Excel formulas are not evaluated; only cell values are extracted","PowerPoint speaker notes and animations are not preserved","DOCX comments and tracked changes are not included in output","Embedded OLE objects (charts, embedded files) are not extracted","Formatting like colors, fonts, and styles are normalized to Markdown equivalents"],"requires":["Python 3.8+ with markitdown package and python-docx, openpyxl, or python-pptx dependencies","Office file accessible via local path or HTTP(S) URL","Temporary directory writable by Node.js process"],"input_types":["application/vnd.openxmlformats-officedocument.wordprocessingml.document","application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","application/vnd.openxmlformats-officedocument.presentationml.presentation","file path (local)","URL (http/https)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","document-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_3","uri":"capability://data.processing.analysis.web.page.html.to.markdown.conversion","name":"web page html to markdown conversion","description":"Converts HTML web pages to Markdown by fetching the page via HTTP(S), parsing the DOM structure, and extracting semantic content while removing boilerplate (navigation, ads, scripts). The markitdown Python library uses BeautifulSoup or similar HTML parsing to identify main content, preserve heading hierarchy, convert links to Markdown syntax, and format lists and tables appropriately.","intents":["Capture web articles or documentation pages as Markdown for offline reading or LLM processing","Extract main content from web pages while filtering out navigation and ads","Build knowledge bases by converting web documentation into Markdown format"],"best_for":["Researchers and developers archiving web content for analysis","Teams building RAG systems that ingest web documentation","Content curators converting web articles into structured Markdown"],"limitations":["JavaScript-rendered content is not executed; only static HTML is parsed (no Selenium or Playwright integration)","Requires network access to fetch remote URLs; cannot process pages behind authentication without credentials","Large pages (>10MB HTML) may cause memory pressure during parsing","Embedded media (videos, interactive elements) are not preserved; only links are extracted","Boilerplate removal heuristics may fail on non-standard page layouts, leaving navigation or ads in output"],"requires":["Python 3.8+ with markitdown and BeautifulSoup4 or similar HTML parsing library","Network connectivity to fetch remote URLs","HTTP(S) URL with valid DNS resolution"],"input_types":["text/html","URL (http/https)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","web-content-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_4","uri":"capability://data.processing.analysis.youtube.video.transcript.to.markdown.conversion","name":"youtube video transcript to markdown conversion","description":"Converts YouTube videos to Markdown by fetching the video transcript (via YouTube's API or transcript extraction library) and formatting it as readable Markdown with timestamps and speaker labels. The markitdown library handles transcript retrieval and formatting, preserving temporal structure and converting timestamps to Markdown comments or inline references.","intents":["Convert video content into searchable, text-based format for LLM processing or knowledge bases","Extract transcripts from educational or technical videos for documentation purposes","Build searchable archives of video content without requiring video playback"],"best_for":["Researchers and students converting educational videos into study materials","Teams building knowledge bases from video content","Content creators repurposing video transcripts into written documentation"],"limitations":["Requires video to have captions/transcripts available; auto-generated transcripts may have accuracy issues","YouTube API rate limits apply; high-volume transcript extraction may hit quotas","Video metadata (duration, uploader, description) is not included in output","Speaker identification relies on YouTube's caption data; may be inaccurate for multi-speaker videos","Non-English videos depend on YouTube's auto-translation quality"],"requires":["Python 3.8+ with markitdown and youtube-transcript-api or similar library","Network connectivity to reach YouTube","Valid YouTube video URL with publicly available transcript"],"input_types":["URL (youtube.com/watch?v=...)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","media-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_5","uri":"capability://image.visual.image.to.markdown.with.ocr.and.description","name":"image to markdown with ocr and description","description":"Converts images (PNG, JPG, etc.) to Markdown by performing optical character recognition (OCR) to extract text content and generating alt-text descriptions. The markitdown library integrates with Python OCR engines (likely Tesseract or similar) to extract text from images and optionally uses vision models to generate semantic descriptions, embedding results as Markdown code blocks or alt-text attributes.","intents":["Extract text from scanned documents or screenshots for searchable Markdown archives","Convert images with embedded text into text-based format for LLM processing","Generate accessible alt-text for images while preserving extracted content"],"best_for":["Document digitization workflows converting scanned images to text","Teams processing screenshots and diagrams for knowledge bases","Accessibility-focused projects generating alt-text for image archives"],"limitations":["OCR accuracy depends on image quality; low-resolution or rotated images may produce garbled text","Handwritten text recognition is limited; printed text works much better","Complex layouts with mixed text and graphics may not preserve spatial relationships","Large images (>50MB) may cause memory pressure during OCR processing","Non-Latin scripts (CJK, Arabic, etc.) require language-specific OCR models"],"requires":["Python 3.8+ with markitdown and Tesseract OCR or similar engine installed","Image file accessible via local path or HTTP(S) URL","Supported image format (PNG, JPG, TIFF, BMP, etc.)"],"input_types":["image/png","image/jpeg","image/tiff","image/bmp","file path (local)","URL (http/https)"],"output_types":["text/markdown"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_6","uri":"capability://data.processing.analysis.audio.file.transcription.to.markdown","name":"audio file transcription to markdown","description":"Converts audio files (MP3, WAV, etc.) to Markdown by transcribing speech to text using Python speech-to-text libraries (likely Whisper or similar). The markitdown library handles audio format detection, transcription, and optional speaker diarization, outputting transcribed text with timestamps and speaker labels formatted as Markdown.","intents":["Convert podcast episodes or meeting recordings into searchable text format","Extract transcripts from audio files for documentation or accessibility purposes","Build searchable archives of audio content without requiring playback"],"best_for":["Researchers and journalists processing interview recordings","Teams documenting meetings and calls as searchable text","Content creators repurposing audio into written documentation"],"limitations":["Transcription accuracy depends on audio quality; background noise significantly degrades output","Large audio files (>1GB) may require significant processing time and memory","Speaker diarization (identifying who spoke when) is approximate and may fail with overlapping speech","Non-English audio requires language-specific models; multilingual content may be misidentified","Real-time transcription not supported; all processing is batch-based"],"requires":["Python 3.8+ with markitdown and OpenAI Whisper or similar speech-to-text library","Audio file accessible via local path or HTTP(S) URL","Supported audio format (MP3, WAV, FLAC, OGG, etc.)","Sufficient disk space for temporary audio processing"],"input_types":["audio/mpeg","audio/wav","audio/flac","audio/ogg","file path (local)","URL (http/https)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","media-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_7","uri":"capability://search.retrieval.bing.search.results.to.markdown.compilation","name":"bing search results to markdown compilation","description":"Converts Bing search results into a compiled Markdown document by querying Bing Search API, fetching the top N results, extracting content from each result page, and aggregating them into a single Markdown file with source attribution. The markitdown library handles search query execution, result ranking, and content extraction from each result, with links and citations preserved in Markdown format.","intents":["Compile research summaries from multiple web sources into a single Markdown document","Aggregate search results into knowledge base entries with proper attribution","Build context documents for LLM processing from web search results"],"best_for":["Researchers gathering information on specific topics from web sources","Teams building knowledge bases from search results","LLM applications needing to augment context with web search"],"limitations":["Requires Bing Search API key and associated costs per query","Search result ranking may not match user relevance expectations","Fetching and parsing all result pages adds significant latency (5-30 seconds typical)","Some websites block automated access; content extraction may fail for protected pages","Result freshness depends on Bing's index; very recent content may not be included","No deduplication of similar content across multiple results"],"requires":["Python 3.8+ with markitdown and Bing Search API client library","Bing Search API key (requires Azure subscription)","Network connectivity to reach Bing and result URLs","Search query string"],"input_types":["text (search query)"],"output_types":["text/markdown"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_8","uri":"capability://data.processing.analysis.markdown.file.passthrough.and.validation","name":"markdown file passthrough and validation","description":"Accepts existing Markdown files and validates them for correctness, optionally normalizing formatting (heading levels, list indentation, code fence syntax). The Markdownify class detects Markdown input by file extension or content inspection and either passes through the content unchanged or applies optional normalization rules, ensuring consistent Markdown formatting across converted and native Markdown sources.","intents":["Validate Markdown files for syntax errors or formatting inconsistencies","Normalize Markdown formatting across mixed sources (native Markdown + converted documents)","Ensure consistent Markdown output regardless of input format"],"best_for":["Teams maintaining Markdown-based documentation with mixed sources","Quality assurance workflows validating Markdown syntax","Content pipelines requiring consistent Markdown formatting"],"limitations":["Validation is basic; does not check for semantic correctness (e.g., broken links, undefined references)","Normalization may alter intentional formatting choices (e.g., custom indentation)","Very large Markdown files (>100MB) may cause memory pressure","No support for Markdown extensions (frontmatter, tables, footnotes) beyond CommonMark"],"requires":["Markdown file accessible via local path or HTTP(S) URL",".md or .markdown file extension"],"input_types":["text/markdown","file path (local)","URL (http/https)"],"output_types":["text/markdown"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-zcaceres--markdownify-mcp__cap_9","uri":"capability://automation.workflow.temporary.file.management.for.url.sourced.content","name":"temporary file management for url-sourced content","description":"Manages the lifecycle of temporary files created when processing remote URLs, downloading content to a temp directory, passing the file path to the markitdown Python tool, and cleaning up after conversion completes. The Markdownify class handles temp directory creation, file naming, cleanup on success/failure, and error handling for disk space issues, abstracting file system complexity from the conversion logic.","intents":["Process remote URLs without requiring users to manually download files","Safely manage temporary storage during conversion without leaving orphaned files","Handle disk space constraints and cleanup failures gracefully"],"best_for":["Systems processing high volumes of remote URLs","Environments with limited disk space or cleanup constraints","Applications requiring reliable temp file cleanup on error"],"limitations":["Temp directory must be writable by Node.js process; permission errors will fail conversions","No built-in disk space checking; large files may exhaust available space","Cleanup failures (e.g., file locks on Windows) may leave orphaned temp files","Concurrent conversions may create many temp files; no built-in limits on temp directory size","No support for temp file encryption; sensitive content is stored unencrypted on disk"],"requires":["Writable temporary directory (system temp or configured path)","Sufficient disk space for largest expected file","Network connectivity to download remote URLs"],"input_types":["URL (http/https)"],"output_types":["file path (local temp file)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Node.js 18+","TypeScript runtime or compiled JavaScript","MCP-compatible client application (Claude Desktop, custom MCP client, etc.)","Python 3.8+ with markitdown package installed via uv","PDF file accessible via local path or HTTP(S) URL","Temporary directory writable by Node.js process for URL-sourced PDFs","Python 3.8+ installed on system","uv package manager installed and in PATH","markitdown Python package installed via uv","Node.js child_process module available"],"failure_modes":["MCP protocol overhead adds ~50-100ms per request compared to direct function calls","Requires MCP-compatible client; cannot be used with REST-only applications without additional adapter","Tool discovery is static at server startup; dynamic tool registration not supported","Complex layouts with multi-column text may not preserve spatial relationships in Markdown","Scanned PDFs without OCR will produce empty or minimal output; OCR not built-in","Large PDFs (>100MB) may cause memory pressure in the Node.js process managing temp files","Embedded images in PDFs are extracted but not embedded in output Markdown; only text content is preserved","Subprocess overhead adds ~100-500ms per conversion compared to direct Python library calls","Large output (>100MB Markdown) may cause memory pressure when buffering stdout","No streaming output; entire result must be buffered before returning to client","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.5161736996679378,"quality":0.34,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.064Z","last_scraped_at":"2026-05-03T13:56:59.049Z","last_commit":"2026-05-01T21:05:12Z"},"community":{"stars":2623,"forks":215,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=zcaceres--markdownify-mcp","compare_url":"https://unfragile.ai/compare?artifact=zcaceres--markdownify-mcp"}},"signature":"bJDRB5bPws3wQR5GQvVp1Ynqu4jafAYadEdnV9pOM+fkfp75HbW/pk70kTtJ5EL2CJDAsx/Z2yzaoob0/JQMAA==","signedAt":"2026-06-21T21:27:06.624Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zcaceres--markdownify-mcp","artifact":"https://unfragile.ai/zcaceres--markdownify-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=zcaceres--markdownify-mcp","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"}}