Python utility and MCP server for converting files (PDF, Office docs, images, audio, HTML, etc.) to Markdown for LLM pipelines.
https://github.com/microsoft/markitdownYou're building with LLMs, but your data is trapped in PDFs, Word docs, PowerPoints, and Excel files. While you could manually copy-paste or use basic extraction tools, you lose all the structure that makes documents meaningful - headings, tables, lists, links.
Microsoft's MarkItDown MCP server solves this by converting virtually any document format into clean, structured Markdown that LLMs actually understand. With 59k+ GitHub stars, this isn't an experimental tool - it's production-ready infrastructure for document processing pipelines.
Instead of feeding your AI assistant raw text dumps that lose context, MarkItDown preserves the document structure that helps LLMs understand relationships between information. When you convert a financial report, you keep the table formatting. When you process meeting notes, you maintain the bullet points and action items.
The output is optimized for token efficiency too. Since mainstream LLMs like GPT-4 natively understand Markdown (they often respond in Markdown unprompted), you're working with their training data format rather than against it.
Office Documents: Word, PowerPoint, Excel - including complex formatting, tables, and embedded content
PDFs: Text extraction with structure preservation, not just raw text dumps
Images: OCR plus EXIF metadata extraction for comprehensive content analysis
Audio Files: Speech transcription with metadata - perfect for meeting recordings
Web Content: HTML with clean conversion that maintains semantic structure
Archives: ZIP files with recursive processing of contained documents
Media: YouTube URLs with transcript extraction
Data Formats: CSV, JSON, XML with intelligent structure mapping
Document Analysis Pipeline
from markitdown import MarkItDown
md = MarkItDown()
# Convert quarterly reports for financial analysis
result = md.convert("Q3_financial_report.pdf")
# Feed structured markdown directly to your LLM
analysis = llm.analyze(result.text_content)
Meeting Intelligence System
# Convert recorded meetings to structured notes
markitdown team_meeting.mp3 -o meeting_notes.md
# Now your AI assistant can extract action items, decisions, and follow-ups
Knowledge Base Ingestion Process entire document libraries without losing the formatting that provides context. Your RAG system gets clean, structured content instead of mangled text dumps.
The MCP server implementation means you can integrate MarkItDown directly into Claude Desktop or any MCP-compatible application. Instead of manual file conversion workflows, your AI assistant can process documents on-demand during conversations.
When you drag a PDF into Claude, it can automatically convert it to structured Markdown and analyze the content with full context awareness. No more "I can't read this file" responses.
Basic Installation
pip install 'markitdown[all]'
markitdown document.pdf -o output.md
Selective Dependencies (for lighter installs)
pip install 'markitdown[pdf,docx,xlsx]' # Just what you need
MCP Server Configuration Point your MCP client to the included server implementation, and document conversion becomes available as a native tool in your AI applications.
Need custom format support? The plugin system lets you extend MarkItDown for proprietary formats or specialized processing needs. Check existing plugins with markitdown --list-plugins or build your own following the sample plugin template.
Azure Document Intelligence Integration: For enterprise-grade OCR and document understanding
Batch Processing: Handle document libraries programmatically
No Temporary Files: Stream processing keeps your filesystem clean
Docker Support: Container-ready for deployment in any environment
Flexible Output: Command-line, Python API, or MCP server - use what fits your stack
While textract extracts text, MarkItDown preserves document structure as Markdown. The difference is semantic understanding vs. raw text extraction. When your LLM processes a converted spreadsheet, it sees properly formatted tables, not comma-separated chaos.
MarkItDown is purpose-built for LLM consumption, not human reading. Every design decision optimizes for downstream AI processing while maintaining the document relationships that provide context.
Your document processing pipeline deserves better than text dumps. MarkItDown gives your LLMs the structured input they need to provide meaningful analysis of your content.