Linked Markitdown

Linked Markitdown is an MCP server built on Microsoft's MarkItDown library that converts a wide range of file formats into clean, structured Markdown text. This MCP bridge enables AI assistants to ingest and process documents such as PDFs, Word files, PowerPoint presentations, Excel spreadsheets, images, audio files, and HTML pages by transforming them into token-efficient Markdown that language models can readily understand and analyze.

When to Use This MCP Server

Connect this server when...

• You need to extract text content from PDFs, DOCX, PPTX, or XLSX files for AI-driven analysis and summarization
• Your workflow involves processing diverse document formats and you want a single unified conversion pipeline
• You want AI assistants to read and interpret scanned documents through OCR capabilities
• You are building document ingestion pipelines that need to convert files into LLM-friendly Markdown format
• You need to transcribe audio files or extract YouTube video transcriptions for text-based processing

Consider alternatives when...

• You only work with plain text or Markdown files that require no conversion
• You need pixel-perfect document rendering rather than text extraction
• Your documents contain primarily tabular data better served by direct database queries

Quick Start

Copy
# .mcp.json configuration
{
  "mcpServers": {
    "markitdown": {
      "command": "npx",
      "args": ["-y", "markitdown-mcp"]
    }
  }
}

Connection setup:

1. Ensure Node.js 18+ is installed on your system
2. Add the configuration above to your .mcp.json file
3. For enhanced PDF support, install Python dependencies: pip install 'markitdown[all]'
4. Restart your MCP client to activate the server

Example tool usage:

# Convert a PDF document to Markdown
> Convert the file at /path/to/research-paper.pdf to Markdown

# Extract content from an Excel spreadsheet
> Read the spreadsheet at /path/to/data.xlsx and show me the tables

# Process a PowerPoint presentation
> Extract all slide content and speaker notes from /path/to/presentation.pptx

Core Concepts

Concept	Purpose	Details
Format Detection	Automatic file type identification	The server inspects file extensions and MIME types to select the appropriate conversion handler for each input file
Markdown Output	Token-efficient text representation	All documents are converted to structured Markdown with headings, tables, lists, and inline formatting preserved
OCR Pipeline	Image text extraction	Optical character recognition extracts text from images and scanned documents using Tesseract or similar engines
LLM Enhancement	AI-powered image descriptions	Optional integration with language models to generate detailed descriptions of images embedded in documents
Stream Processing	Memory-efficient conversion	Large files are processed via streaming to handle documents that exceed available memory

Architecture:

+------------------+       +------------------+       +------------------+
|  Input Files     |       |  MarkItDown      |       |  MCP Server      |
|  PDF/DOCX/PPTX   |------>|  Conversion      |------>|  (stdio)         |
|  XLSX/IMG/Audio   |       |  Engine          |       +------------------+
+------------------+       +------------------+              |
                              |   |   |                 stdio |
                           OCR  LLM  Plugin              v
                                                   +------------------+
                                                   |  AI Assistant    |
                                                   +------------------+

Configuration

Parameter	Type	Default	Description
llm_client	string	none	Optional OpenAI-compatible client for AI-enhanced image descriptions in presentations
llm_model	string	none	Model identifier for image description generation (e.g., claude-sonnet-4.5)
docintel_endpoint	string	none	Azure Document Intelligence endpoint for enhanced PDF extraction
use_plugins	boolean	false	Enable third-party MarkItDown plugins for extended format support
ocr_engine	string	tesseract	OCR engine selection for image and scanned document text extraction

Best Practices

1. Install format-specific dependencies. Rather than installing the full dependency set, install only the format packages you need with pip install 'markitdown[pdf,docx]'. This reduces the server footprint and avoids unnecessary dependency conflicts in your environment.

2. Use AI-enhanced descriptions for visual content. When processing presentations or image-heavy documents, configure the LLM client to generate detailed image descriptions. This ensures that charts, diagrams, and photographs in your documents are meaningfully represented in the Markdown output rather than being silently dropped.

3. Handle large files with streaming. For documents exceeding 50MB, use the stream-based conversion method to avoid memory pressure. The server supports reading from file streams rather than loading entire documents into memory, which is essential for batch processing workflows.

4. Validate output structure for downstream processing. After conversion, verify that tables, headings, and lists are correctly structured in the Markdown output. Some complex document layouts may require post-processing to clean up formatting artifacts from the conversion process.

5. Keep the server updated for format compatibility. The MarkItDown library receives regular updates that improve conversion quality and add support for new file formats. Run pip install --upgrade markitdown periodically to ensure you have the latest conversion capabilities and bug fixes.

Common Issues

PDF conversion produces garbled text. This usually indicates the PDF contains scanned images rather than selectable text. Install Tesseract OCR (brew install tesseract on macOS or apt-get install tesseract-ocr on Linux) and ensure the OCR pipeline is enabled. For complex layouts, consider using Azure Document Intelligence for superior extraction quality.

PowerPoint images are missing from output. By default, images in presentations are represented by placeholder text unless an LLM client is configured. Add llm_client and llm_model parameters to your configuration to enable AI-powered image description generation, which produces detailed textual descriptions of each image.

Server fails to start with dependency errors. The MarkItDown server requires Python dependencies for certain file formats. If you see import errors, install the specific format package: pip install 'markitdown[pdf]' for PDF support, pip install 'markitdown[docx]' for Word documents, and so on. Running pip install 'markitdown[all]' resolves most dependency issues at once.