Website to Markdown MCP Server
Pricing
from $1.00 / 1,000 url fetcheds
Website to Markdown MCP Server
Convert any webpage to clean Markdown. MCP server for AI agents and LLM pipelines.
Pricing
from $1.00 / 1,000 url fetcheds
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Developer
Marek Pommier
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4 days ago
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Convert any webpage to clean Markdown. MCP server for AI agents and LLM pipelines.
MCP tools
| Tool | What it does |
|---|---|
get_markdown | Fetch a webpage and convert it to clean Markdown. Returns the markdown content. |
get_markdown_batch | Fetch multiple webpages and convert each to Markdown. Returns list of {url, markdown, error}. |
extract_metadata | Extract page metadata (title, description, author, etc.) from a URL. |
Connect from your AI agent
Streamable HTTP endpoint:
https://quodlibetical-buffalo--website-to-markdown-mcp.apify.actor/mcp
Claude Desktop / Cursor / any MCP client:
{"mcpServers": {"website-to-markdown-mcp": {"type": "streamable-http","url": "https://quodlibetical-buffalo--website-to-markdown-mcp.apify.actor/mcp","headers": {"Authorization": "Bearer YOUR_APIFY_TOKEN"}}}}
Standby endpoints require an Apify API token — pass any Apify token (apify_api_...) as a Bearer header or as a token query parameter.
Input (batch run)
| Field | Type | Default | Description |
|---|---|---|---|
url | string | https://example.com | The URL of the webpage to convert to Markdown. |
urls | array | [] | Array of URLs to convert to Markdown (batch mode). |
mode | string | batch | Legacy switch. Normal runs always execute batch mode; the MCP server is started automatically only when the platform starts this Actor in Standby. |
maxContentLength | integer | 50000 | Maximum characters to extract per page. |
Run it on Apify with default input {} to get a demo record in the dataset.
Pricing (pay-per-event)
- actor-start — Actor Start: $0.01 per event
- url-fetched — URL Fetched: $0.001 per event
Pay only for events. Prices are set ~30% below the market benchmark for this category.
Notes
Default run converts https://example.com to Markdown as a demo. Pass any URL to get clean, agent-ready Markdown back.
Run it as an Actor
Start a run with empty input {} — the Actor executes its batch mode, pushes one demo record to the dataset and exits successfully.