
MarkGrab
- Updated April 15, 2026
- QuartzUnit/markgrab
MarkGrab is an MCP server that converts URLs—including HTML, YouTube, PDF, and DOCX—into LLM-ready markdown.
About
MarkGrab is a Model Context Protocol server for universal web and document content extraction: point it at a URL and get markdown suitable for LLM reasoning, covering HTML, YouTube, PDF, and DOCX per its description. developers use it in the idea phase to absorb competitor sites and references, during build when agents need normalized external content in pipelines, and in grow when turning sources into content assets. Register the stdio server from PyPI in Claude Code or Cursor, call the extraction tools, and feed the markdown into summarization, spec drafting, or RAG-style workflows. It is a fetch-and-clean layer, not a schema JSON extractor—pair with docpick when you need typed fields from scans rather than readable prose.
- Universal extraction: HTML pages, YouTube, PDF, and DOCX URLs to markdown
- LLM-ready markdown output designed for agent context windows
- markgrab PyPI package (0.1.2) with stdio MCP transport
- Reduces manual copy-paste and brittle one-off scrapers during research
- GitHub repository QuartzUnit/markgrab
MarkGrab by the numbers
- Data as of Jul 7, 2026 (Skillselion catalog sync)
claude mcp add markgrab -- uvx markgrabAdd your badge
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| Package | markgrab |
|---|---|
| Transport | STDIO |
| Auth | None |
| Last updated | April 15, 2026 |
| Repository | QuartzUnit/markgrab ↗ |
What it does
Convert almost any URL—HTML, YouTube, PDF, DOCX—into LLM-ready markdown from your agent via MCP.
Who is it for?
Best when you constantly pull docs, landing pages, papers, and videos into agent context without writing custom scrapers per format.
Skip if: Workflows that need schema-locked JSON from scanned forms, or teams blocked from fetching arbitrary URLs by compliance policies.
What you get
After you connect MarkGrab, your agent can request a URL and receive markdown you can cite, summarize, or drop into build and content tasks.
- LLM-ready markdown from supported URL types (HTML, YouTube, PDF, DOCX)
- Agent-callable extraction without per-site scraper scripts
- Cleaner research corpora for ideation and content pipelines
By the numbers
- MCP server version 0.1.2
- Supports HTML, YouTube, PDF, and DOCX sources per server description
- PyPI identifier markgrab, stdio transport
README.md
MarkGrab
Universal web content extraction — any URL to LLM-ready markdown.
from markgrab import extract
result = await extract("https://example.com/article")
print(result.markdown) # clean markdown
print(result.title) # "Article Title"
print(result.word_count) # 1234
print(result.language) # "en"
Features
- HTML — BeautifulSoup + content density filtering (removes nav, sidebar, ads)
- YouTube — transcript extraction with timestamps
- PDF — text extraction with page structure
- DOCX — paragraph and heading extraction
- Auto-fallback — tries lightweight httpx first, falls back to Playwright for JS-heavy pages
- Async-first — built on httpx and Playwright async APIs
Install
pip install markgrab
Optional extras for specific content types:
pip install "markgrab[browser]" # Playwright for JS-rendered pages
pip install "markgrab[youtube]" # YouTube transcript extraction
pip install "markgrab[pdf]" # PDF text extraction
pip install "markgrab[docx]" # DOCX text extraction
pip install "markgrab[all]" # everything
Usage
Python API
import asyncio
from markgrab import extract
async def main():
# HTML (auto-detects content type)
result = await extract("https://example.com/article")
# YouTube transcript
result = await extract("https://youtube.com/watch?v=dQw4w9WgXcQ")
# PDF
result = await extract("https://arxiv.org/pdf/1706.03762")
# Options
result = await extract(
"https://example.com",
max_chars=30_000, # limit output length (default: 50K)
use_browser=True, # force Playwright rendering
stealth=True, # anti-bot stealth scripts (opt-in)
timeout=60.0, # request timeout in seconds
proxy="http://proxy:8080",
)
asyncio.run(main())
CLI
markgrab https://example.com # markdown output
markgrab https://example.com -f text # plain text
markgrab https://example.com -f json # structured JSON
markgrab https://example.com --browser # force browser rendering
markgrab https://example.com --max-chars 10000 # limit output
ExtractResult
result.title # page title
result.text # plain text
result.markdown # LLM-ready markdown
result.word_count # word count
result.language # detected language ("en", "ko", ...)
result.content_type # "article", "video", "pdf", "docx"
result.source_url # final URL (after redirects)
result.metadata # extra metadata (video_id, page_count, etc.)
How it works
flowchart TD
A["🔗 URL Input"] --> B{"Content\nType?"}
B -->|"HTML"| C["HTTP fetch\n(httpx)"]
C --> D{"JS\nrequired?"}
D -->|"no"| E["HTML Parser\n→ clean markdown"]
D -->|"yes"| F["Playwright\nfallback"]
F --> E
B -->|"YouTube"| G["Transcript API\n→ timestamped markdown"]
B -->|"PDF"| H["PDF Parser\n→ structured markdown"]
B -->|"DOCX"| I["DOCX Parser\n→ markdown"]
E --> J["✅ LLM-ready\nMarkdown"]
G --> J
H --> J
I --> J
For HTML pages, if the initial httpx fetch yields fewer than 50 words, MarkGrab automatically retries with Playwright to handle JavaScript-rendered content.
Disclaimer
This software is provided for legitimate purposes only. By using MarkGrab, you agree to the following:
robots.txt: MarkGrab does not check or enforce
robots.txt. Users are solely responsible for checking and respectingrobots.txtdirectives and the terms of service of any website they access.Rate limiting: MarkGrab does not include built-in rate limiting or request throttling. Users must implement their own rate limiting to avoid overloading target servers. Abusive request patterns may violate applicable laws and website terms of service.
YouTube transcripts: YouTube transcript extraction relies on the third-party
youtube-transcript-apilibrary, which uses YouTube's internal (unofficial) caption API. This may not comply with YouTube's Terms of Service. Use at your own discretion and risk.Stealth mode: The optional
stealth=Truefeature modifies browser fingerprinting signals to reduce bot detection. This feature is intended for legitimate use cases such as testing, research, and accessing content that is publicly available to regular browser users. Users are responsible for ensuring their use complies with applicable laws and the terms of service of target websites.Legal compliance: Users are responsible for ensuring that their use of MarkGrab complies with all applicable laws, including but not limited to the Computer Fraud and Abuse Act (CFAA), the Digital Millennium Copyright Act (DMCA), GDPR, and equivalent legislation in their jurisdiction.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND. See the LICENSE file for the full MIT license text.
Acknowledgments
MarkGrab builds on excellent open-source work and well-established techniques:
- puppeteer-extra-plugin-stealth — stealth evasion patterns (webdriver removal, plugin mocking, WebGL spoofing) that inspired the opt-in
anti_bot/stealth.pymodule - Mozilla Readability — content area detection priority (
article > main > body) and link density filtering concepts used in the density filter - Boilerpipe (Kohlschutter et al., 2010) — the academic origin of link density ratio algorithms for boilerplate removal
- Jina Reader — validated the market need for URL-to-markdown extraction; MarkGrab aims to be a lightweight, self-hosted alternative
Built with httpx, BeautifulSoup, markdownify, Playwright, youtube-transcript-api, pdfplumber, and python-docx.
Used in
- newswatch — RSS news monitoring pipeline (feedkit → markgrab → embgrep → diffgrab)
- watchdeck — Web page monitoring with visual diffs and safety guards
License
Part of the QuartzUnit ecosystem — composable Python libraries for data collection, extraction, search, and AI agent safety.
Recommended MCP Servers
How it compares
URL-to-markdown web extraction MCP, not local semantic repo search or curated RSS-only research.
FAQ
Who is markgrab for?
Developers and agent users who research on the open web and need consistent markdown from mixed URL types inside MCP clients.
When should I use markgrab?
Use it whenever you have a link to a page, video, PDF, or DOCX and want LLM-ready markdown for analysis, specs, or content without manual cleanup.
How do I add markgrab to my agent?
Install markgrab from PyPI (0.1.2), add the MCP stdio server entry with identifier markgrab, restart your agent host, and invoke extraction tools with target URLs.