What is Skrape Mcp

Skrape-MCP is an MCP Server designed for skrape.ai that converts any webpage into clean, LLM-ready Markdown format. It is ideal for integrating web content into Large Language Models (LLMs).

Use cases

Use cases for Skrape-MCP include converting blog posts, articles, and other web content into Markdown for LLM training, research, or content generation, as well as integrating web data into applications that utilize LLMs.

How to use

To use Skrape-MCP, install it via Smithery or manually by obtaining an API key from skrape.ai, installing dependencies, and configuring it in your LLM application. You can then call the ‘get_markdown’ tool with the desired URL to convert the webpage into Markdown.

Key features

Key features include clean and structured Markdown output, noise reduction by removing ads and irrelevant content, consistent formatting, JavaScript support for dynamic content, and optimization for LLMs like Claude and GPT.

Where to use

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Skrape MCP Server

Convert any webpage into clean, LLM-ready Markdown using skrape.ai. Perfect for feeding web content into LLMs.

This MCP server provides a simple interface to convert web pages to structured, clean Markdown format using the skrape.ai API. It’s designed to work seamlessly with Claude Desktop, other LLMs, and MCP-compatible applications.

Why Use Skrape for LLM Integration?

• Clean, Structured Output: Generates well-formatted Markdown that’s ideal for LLM consumption
• Noise Reduction: Automatically removes ads, navigation menus, and other irrelevant content
• Consistent Format: Ensures web content is uniformly structured regardless of the source
• JavaScript Support: Handles dynamic content by rendering JavaScript before conversion
• LLM-Optimized: Perfect for feeding web content into LLMs like Claude, GPT, and other LLM models

Features

Tools

• get_markdown - Convert any webpage to LLM-ready Markdown
  • Takes any input URL and optional parameters
  • Returns clean, structured Markdown optimized for LLM consumption
  • Supports JavaScript rendering for dynamic content
  • Optional JSON response format for advanced integrations

Installation

Installing via Smithery

To install Skrape MCP Server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @skrapeai/skrape-mcp --client claude

Manual Installation

1. Get your API key from skrape.ai

2. Install dependencies:

npm install

1. Build the server:

npm run build

1. Add the server config to Claude Desktop:

On MacOS:

nano ~/Library/Application\ Support/Claude/claude_desktop_config.json

On Windows:

notepad %APPDATA%/Claude/claude_desktop_config.json

Add this configuration (replace paths and API key with your values):

Using with LLMs

Here’s how to use the server with Claude or other LLM models:

1. First, ensure the server is properly configured in your LLM application
2. Then, you can ask the ALLMI to fetch and process any webpage:

Convert this webpage to markdown: https://example.com

Claude will use the MCP tool like this:
<use_mcp_tool>
<server_name>skrape</server_name>
<tool_name>get_markdown</tool_name>
<arguments>
{
  "url": "https://example.com",
  "options": {
    "renderJs": true
  }
}
</arguments>
</use_mcp_tool>

The resulting Markdown will be clean, structured, and ready for LLM processing.

Advanced Options

The get_markdown tool accepts these parameters:

• url (required): Any webpage URL to convert
• returnJson (optional): Set to true to get the full JSON response instead of just markdown
• options (optional): Additional scraping options
  • renderJs: Whether to render JavaScript before scraping (default: true)

Example with all options:

<use_mcp_tool>
<server_name>skrape</server_name>
<tool_name>get_markdown</tool_name>
<arguments>
{
  "url": "https://example.com",
  "returnJson": true,
  "options": {
    "renderJs": false
  }
}
</arguments>
</use_mcp_tool>

Development

For development with auto-rebuild:

npm run watch

Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the MCP Inspector:

npm run inspector

The Inspector will provide a URL to access debugging tools in your browser.