by microsoft
Easily convert markdown to PDF using Markitdown MCP server. Supports HTTP, STDIO, and SSE for fast converting markdown t
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Converts various file types from URLs or local files into markdown format. Designed specifically for preparing documents for LLM processing while preserving structure like headings, tables, and lists.
Markitdown is an official MCP server published by microsoft that provides AI assistants with tools and capabilities via the Model Context Protocol. Easily convert markdown to PDF using Markitdown MCP server. Supports HTTP, STDIO, and SSE for fast converting markdown t This server exposes 1 tool that AI clients can invoke during conversations and coding sessions.
You can install Markitdown in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Markitdown is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Markitdown has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Markitdown is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Markitdown reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: Markitdown is the kind of server we cite when onboarding engineers to host + tool permissions.
Markitdown reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We evaluated Markitdown against two servers with overlapping tools; this profile had the clearer scope statement.
Markitdown has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Markitdown reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Markitdown is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: Markitdown surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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[!TIP] MarkItDown now offers an MCP (Model Context Protocol) server for integration with LLM applications like Claude Desktop. See markitdown-mcp for more information.
[!IMPORTANT] Breaking changes between 0.0.1 to 0.1.0:
- Dependencies are now organized into optional feature-groups (further details below). Use
pip install 'markitdown[all]'to have backward-compatible behavior.- convert_stream() now requires a binary file-like object (e.g., a file opened in binary mode, or an io.BytesIO object). This is a breaking change from the previous version, where it previously also accepted text file-like objects, like io.StringIO.
- The DocumentConverter class interface has changed to read from file-like streams rather than file paths. No temporary files are created anymore. If you are the maintainer of a plugin, or custom DocumentConverter, you likely need to update your code. Otherwise, if only using the MarkItDown class or CLI (as in these examples), you should not need to change anything.
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on preserving important document structure and content as Markdown (including: headings, lists, tables, links, etc.) While the output is often reasonably presentable and human-friendly, it is meant to be consumed by text analysis tools -- and may not be the best option for high-fidelity document conversions for human consumption.
MarkItDown currently supports the conversion from:
Markdown is extremely close to plain text, with minimal markup or formatting, but still provides a way to represent important document structure. Mainstream LLMs, such as OpenAI's GPT-4o, natively "speak" Markdown, and often incorporate Markdown into their responses unprompted. This suggests that they have been trained on vast amounts of Markdown-formatted text, and understand it well. As a side benefit, Markdown conventions are also highly token-efficient.
MarkItDown requires Python 3.10 or higher. It is recommended to use a virtual environment to avoid dependency conflicts.
With the standard Python installation, you can create and activate a virtual environment using the following commands:
python -m venv .venv
source .venv/bin/activate
If using uv, you can create a virtual environment with:
uv venv --python=3.12 .venv
source .venv/bin/activate
# NOTE: Be sure to use 'uv pip install' rather than just 'pip install' to install packages in this virtual environment
If you are using Anaconda, you can create a virtual environment with:
conda create -n markitdown python=3.12
conda activate markitdown
To install MarkItDown, use pip: pip install 'markitdown[all]'. Alternatively, you can install it from the source:
git clone [email protected]:microsoft/markitdown.git
cd markitdown
pip install -e 'packages/markitdown[all]'
markitdown path-to-file.pdf > document.md
Or use -o to specify the output file:
markitdown path-to-file.pdf -o document.md
You can also pipe content:
cat path-to-file.pdf | markitdown
MarkItDown has optional dependencies for activating various file formats. Earlier in this document, we installed all optional dependencies with the [all] option. However, you can also install them individually for more control. For example:
pip install 'markitdown[pdf, docx, pptx]'
will install only the dependencies for PDF, DOCX, and PPTX files.
At the moment, the following optional dependencies are available:
[all] Installs all optional dependencies[pptx] Installs dependencies for PowerPoint files[docx] Installs dependencies for Word files[xlsx] Installs dependencies for Excel files[xls] Installs dependencies for older Excel files[pdf] Installs dependencies for PDF files[outlook] Installs dependencies for Outlook messages[az-doc-intel] Installs dependencies for Azure Document Intelligence[audio-transcription] Installs dependencies for audio transcription of wav and mp3 files[youtube-transcription] Installs dependencies for fetching YouTube video transcriptionMarkItDown also supports 3rd-party plugins. Plugins are disabled by default. To list installed plugins:
markitdown --list-plugins
To enable plugins use:
markitdown --use-plugins path-to-file.pdf
To find available plugins, search GitHub for the hashtag #markitdown-plugin. To develop a plugin, see packages/markitdown-sample-plugin.
To use Microsoft Document Intelligence for conversion:
markitdown path-to-file.pdf -o document.md -d -e "<document_intelligence_endpoint>"
More information about how to set up an Azure Document Intelligence Resource can be found here
Basic usage in Python:
from markitdown import MarkItDown
md = MarkItDown(enable_plugins=False) # Set to True to enable plugins
result = md.convert("test.xlsx")
print(result.text_content)
Document Intelligence conversion in Python:
from markitdown import MarkItDown
md = MarkItDown(docintel_endpoint="<document_intelligence_endpoint>")
result = md.convert("test.pdf")
print(result.text_content)
To use Large Language Models for image descriptions (currently only for pptx and image files), provide llm_client and llm_model:
from markitdown import MarkItDown
from openai import OpenAI
client = OpenAI()
md = MarkItDown(llm_client=client, llm_model="gpt-4o", llm_prompt="optional custom prompt")
result = md.convert("example.jpg")
print(result.text_content)
docker build -t markitdown:latest .
docker run --rm -i markitdown:latest < ~/your-file.pdf > output.md
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
You can help by looking at issues or helping review PRs. Any issue or PR is welcome, but we have also marked some as 'open for contribution' and 'open for reviewing' to help facilitate community contributions. These are of course just suggestions and you are welcome to contribute in any way you like.
<div align="center">| All | Especially Needs Help from Community | |
|---|---|---|
| Issues | All Issues | Issues open for contribution |
| PRs | All PRs | PRs open for reviewing |
Navigate to the MarkItDown package:
cd packages/markitdown
Install hatch in your environment and run tests:
pip install hatch # Other ways of installing hatch: https://hatch.pypa.io/dev/install/
hatch shell
hatch test
(Alternative) Use the Devcontainer which has all the dependencies installed:
# Reopen the project in Devcontainer and run:
hatch test
Run pre-commit checks before submitting a PR: pre-commit run --all-files
You can also contribute by creating and sharing 3rd party plugins. See packages/markitdown-sample-plugin for more details.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos i
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.