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@mcp-listing/html-markdown-converter-mcp

v1.0.0

Published

HTML to Markdown conversion Model Context Protocol (MCP) server. Accurately transform web content into structured markdown.

Readme

HTML to Markdown Converter MCP

An MCP server optimized for LLM token efficiency, converting raw HTML into clean, well-formatted Markdown.

This tool is especially useful for AI agents because parsing raw HTML consumes a massive amount of tokens and often causes hallucination or context window exhaustion. By converting HTML to Markdown, you save up to 80% on tokens and provide the LLM with a highly structured, readable format.

If you don't want to run this locally, you can use the hosted version for free on Vinkius Cloud: HTML Markdown Converter MCP on Vinkius.

Tools Available

  • convert_html_to_markdown: Converts raw HTML strings into clean Markdown deterministically without LLM truncation or data loss. Preserves headings, links, lists, and code blocks.

Why Use This MCP?

  1. Token Efficiency: LLMs struggle with the noise of raw HTML tags. Markdown is their native language and is far more token-efficient.
  2. Deterministic Processing: Bypasses LLM hallucinations when extracting data from complex DOM structures.
  3. Data Preservation: Accurately maintains headings, lists, links, images, and code blocks during the conversion.

How to use

1. Free Edge Hosting (Recommended)

You do not need to host this yourself! Vinkius provides FREE, highly available edge hosting for MCP servers. You can connect directly via the Vinkius Cloud Marketplace.

Alternatively, you can deploy this exact server to our secure V8 isolate cloud in seconds:

npx mcpfusion deploy

This command bundles your code and instantly deploys it to the Vinkius Edge, providing you with a live, DDoS-protected URL ready to be consumed by your AI agents.

2. Run Locally

  1. Install dependencies:
npm install
  1. Build the server:
npm run build
  1. Run the development server:
npm run dev

Built with MCP Fusion

This server was built using the MCP Fusion framework, ensuring high stability, typesafe models, and strict adherence to the Model Context Protocol.