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Health: ActiveRecent health check succeeded.Last checked 9/11/2026, 5:48:09 PM

Markdownify MCP

User RatingsBe the first to rate and review this MCP server!
View Repository3.0k GitHub StarsTotal stargazers on GitHub for the source repository (2,990 stars).
markdownfile-conversionweb-scrapingtranscriptiondocument-processing

Converts documents, media, web pages, and search results into Markdown through MCP tools.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON â–¾
No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for zcaceres/markdownify-mcp, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.
Install Tool Schemas (10) Directory Badge Claim listing Alternatives🧮 More in Data Science Tools

Overview

The zcaceres/markdownify-mcp MCP server converts files and web content into Markdown for use by MCP-compatible AI clients. It exposes separate tools for PDFs, office documents, images, audio transcription, YouTube transcripts, Bing results, web pages, and existing Markdown files. Run it locally with Bun or Docker, with optional path restrictions for file access. Reach for it when an agent needs to inspect heterogeneous source material in a consistent text format.

Use cases

•Convert PDFs into Markdown for agent review
•Extract transcripts from YouTube videos and audio files
•Turn office documents into Markdown text
•Fetch web pages and Bing results as Markdown
•Retrieve existing Markdown files from permitted directories

Key features

•PDF, DOCX, XLSX, and PPTX conversion
•Image conversion with metadata
•Audio conversion with transcription
•YouTube, Bing, and web page conversion
•Markdown file retrieval
•Optional filesystem path restrictions

Capabilities & Tool Schemas (10) ~142 tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Self-reported Self-reportedParsed from the repository README, not verified against a live server — may be incomplete or out of date.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Markdownify MCP.

youtube-to-markdown

Convert YouTube videos to Markdown

pdf-to-markdown

Convert PDF files to Markdown

bing-search-to-markdown

Convert Bing search results to Markdown

webpage-to-markdown

Convert web pages to Markdown

image-to-markdown

Convert images to Markdown with metadata

audio-to-markdown

Convert audio files to Markdown with transcription

How Markdownify MCP works

What the zcaceres/markdownify-mcp MCP server does

The zcaceres/markdownify-mcp MCP server turns several document, media, and web inputs into Markdown. Its MCP tools cover PDF, DOCX, XLSX, and PPTX files; images with metadata; audio with transcription; YouTube videos; Bing search results; and general web pages. A separate tool retrieves an existing Markdown file when its extension is .md or .markdown.

The output is intended to give an AI client a readable text representation of source material without requiring each client workflow to implement its own file or web conversion logic. The repository uses the markitdown executable for conversion-related work and includes support for a broader set of formats in the local installation.

How it works

The server runs as a local MCP process and exposes conversion operations as callable tools. A client sends an input such as a file path, web page, search request, or YouTube video to the corresponding tool, and the server returns Markdown-formatted content. Audio conversion includes transcription, while image conversion includes image metadata.

For file-based operations, the server can enforce a read boundary. Set MD_ALLOWED_PATHS to one or more permitted directories; path separators are : on POSIX systems and ; on Windows. MD_SHARE_DIR remains available as a deprecated single-directory alias. Without either setting, the README describes file access as unrestricted.

Setup and configuration

The documented local setup is:

  1. Clone the repository.
  2. Run bun install to install dependencies and create the Python virtual environment containing markitdown[all].
  3. Run bun run build.
  4. Start the server with bun start.

The server can also run in Docker. The documented image mounts host directories into the container and sets MD_ALLOWED_PATHS to the corresponding container paths. File tools must receive container paths rather than the original host paths.

The supported configuration variables are:

  • MARKITDOWN_PATH: selects the markitdown executable, overriding the bundled virtual-environment path.
  • REPOMIX_PATH: selects the repomix executable used by git-repo-to-markdown.
  • MD_ALLOWED_PATHS: restricts file-input tools to specified directories.
  • MD_SHARE_DIR: deprecated compatibility alias for a single allowed directory.

Tools and capabilities

The zcaceres/markdownify-mcp MCP server provides these tools:

  • youtube-to-markdown for YouTube videos
  • pdf-to-markdown for PDF files
  • bing-search-to-markdown for Bing search results
  • webpage-to-markdown for web pages
  • image-to-markdown for images and metadata
  • audio-to-markdown for audio files and transcription
  • docx-to-markdown for DOCX files
  • xlsx-to-markdown for XLSX files
  • pptx-to-markdown for PPTX files
  • get-markdown-file for existing .md and .markdown files

Limitations and notes

The published Docker image installs markitdown[pdf] rather than the full extras set. As a result, the README notes that audio transcription and image OCR require the local installation with markitdown[all] and may fail in the slim Docker image.

The desktop-client configuration uses the built dist/index.js file and requires an absolute path to that file. Docker users must mount every host directory the server needs to read and pass the mounted container location to the tools. The repository is licensed under MIT.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
2k
Stargazers on the source repository.
Last commit
1mo ago
Most recent push to the default branch.
Tools exposed
10
Callable tools this server registers over MCP.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Markdownify MCP

Clone the repository, run `bun install`, build with `bun run build`, and start it with `bun start`.

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Technical Specs & Signals

Category🧮Data Science Tools
PricingFree
More technical detailsExpand â–¾
AuthNo auth required
LicenseMIT
Last updatedSep 7, 2026
Views3
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars2,990
GitHub Star CountTotal stargazers on GitHub representing community popularity (2,990 stars).
Last commit1mo ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 11, 2026
52Quality signal: Good · 52/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools21/30
Adoption & activity8/15
Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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