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MCP Server

docling

by docling-project64.9kPythonUpdated 2026-08-15

Get your documents ready for gen AI

Claude Desktop

Docling is an MCP server that parses and converts documents from multiple formats into AI-ready structured content. It handles PDFs with advanced understanding of layout, tables, and formulas, as well as DOCX, PPTX, XLSX, HTML, images, audio, video, and many other formats. The server provides seamless integration with AI agents through the Model Context Protocol, enabling LLMs to extract and work with document content. Developers use it to feed clean, structured document data into generative AI workflows via LangChain, LlamaIndex, Crew AI, Haystack, and MCP-compatible clients.

Key Features

Parses 20+ document formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, audio (WAV, MP3), video (MP4, AVI, MOV), and email files (EML, MSG)
Advanced PDF understanding with page layout analysis, reading order detection, table structure extraction, code blocks, formulas, and image classification
Exports to multiple formats including Markdown, HTML, JSON, WebVTT, DocLang, DocTags, and application-specific XML schemas (USPTO, JATS, XBRL)
OCR support for scanned PDFs and images with Visual Language Models like GraniteDocling
Audio and video processing with Automatic Speech Recognition (ASR) transcripts and keyframe extraction
Local execution capability for sensitive data and air-gapped environments
Native integrations with LangChain, LlamaIndex, Crew AI, and Haystack for agentic AI workflows
MCP server interface for connecting to AI agents and assistants

Use Cases

  • 01Converting research papers and technical PDFs into Markdown for RAG pipelines and LLM context
  • 02Extracting structured data from business documents, financial reports (XBRL), and patent filings (USPTO) for AI analysis
  • 03Processing scanned documents and images with OCR to make legacy content accessible to AI agents
  • 04Transcribing audio and video files into text for content indexing and search applications
  • 05Feeding email archives and presentations into knowledge bases for AI-powered document Q&A
  • 06Building document processing workflows in agentic AI frameworks like LangChain and LlamaIndex

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docling — FAQ

What is the Docling MCP server?+

Docling is an MCP server that converts documents from 20+ formats (PDF, DOCX, images, audio, video, etc.) into structured content that AI agents can consume. It provides advanced PDF understanding, OCR, and export to Markdown, JSON, and other formats through the Model Context Protocol.

How do I install the Docling MCP server?+

Install Docling via pip using 'pip install docling', then configure your MCP client to launch it. Python 3.10 or higher is required; the package works on macOS, Linux, and Windows for both x86_64 and arm64 architectures.

Which AI clients work with Docling?+

Docling works with any MCP-compatible client and has native integrations with LangChain, LlamaIndex, Crew AI, and Haystack. It can also run as a standalone API server (docling-serve) for broader integration scenarios.

Do I need API keys to use Docling?+

No API keys are required for core document processing. Docling runs locally and supports air-gapped environments. If you use optional Visual Language Models or ASR features, model-specific requirements may apply.

Is Docling free and open source?+

Yes, the Docling codebase is released under the MIT license and is free to use. Individual models integrated with Docling may have their own licenses; refer to the original model packages for details.

What formats can Docling convert?+

Docling supports PDF, DOCX, PPTX, XLSX, HTML, EPUB, ODF (ODT, ODS, ODP), images (PNG, JPEG, TIFF), audio (WAV, MP3), video (MP4, AVI, MOV, MKV, WebM), email (EML, MSG), LaTeX, plain text, Markdown, and specialized formats like XBRL and USPTO patents.

How do I install docling?+

Open the source repository on GitHub and follow its README. docling is a mcp server — MCP Agents Market links you directly to the official repo.

Is docling free?+

docling is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.

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