skrape mcp

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):

{
  "mcpServers": {
    "skrape": {
      "command": "node",
      "args": ["path/to/skrape-mcp/build/index.js"],
      "env": {
        "SKRAPE_API_KEY": "your-key-here"
      },
    }
  }
}

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.