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skrapeai

Skrape MCP Server

Official
by skrapeai
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JavaScript
Remote

Skrape MCP Server

Convert webpages into clean, LLM-ready Markdown using skrape.ai. An MCP server that seamlessly integrates web scraping with Claude Desktop and other MCP-compatible applications.

Key Features

  • Clean Output: Removes ads, navigation, and irrelevant content

  • JavaScript Support: Handles dynamic content rendering

  • LLM-Optimized: Structured Markdown perfect for AI consumption

  • Consistent Format: Uniform structure regardless of source

Related MCP server: Scraper MCP

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.

Available Tools

1 tool
get_markdownC

Get markdown content from a webpage using skrape.ai

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the webpage to scrape
returnJsonNoWhether to return JSON response (true) or raw markdown (false)
optionsNoAdditional scraping options

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool uses skrape.ai but doesn't describe rate limits, authentication needs, error handling, or what happens if scraping fails. For a web scraping tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste. It's appropriately sized and front-loaded, clearly stating the core functionality without unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of web scraping (potential for errors, rate limits, etc.), no annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like what the return format looks like, error conditions, or usage constraints, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any meaning beyond what the input schema provides, such as explaining trade-offs between JSON vs. raw markdown or when to adjust JavaScript rendering. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get markdown content from a webpage using skrape.ai'. It specifies the action (get), resource (markdown content), and method (using skrape.ai). However, it doesn't differentiate from siblings since there are none, so it can't earn a 5 for that criterion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or limitations. It merely states what the tool does without context about appropriate scenarios or constraints.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observedget_markdown
Install Server
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TDQS

B3/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or confusion between tools. The tool's purpose is clearly defined as retrieving markdown content from webpages, making it distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'get_markdown' follows a clear verb_noun pattern, which would be consistent if more tools were added.

Tool Count2/5

A single tool is too few for a server named 'Skrape MCP Server', which suggests a broader scraping or data extraction purpose. This minimal toolset limits functionality and feels incomplete for the implied scope, as it only handles markdown retrieval without other common scraping operations.

Completeness1/5

The server is severely incomplete for a scraping domain. It lacks basic operations such as fetching HTML, extracting specific elements, handling different content types, or managing sessions. With only one tool for markdown, agents will face dead ends when trying to perform typical scraping tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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Related MCP Connectors

Related MCP Servers

  • Scraper MCP

    cotdp
    A
    license
    Not graded
    quality
    D
    maintenance
    A context-optimized web scraping server that converts HTML to markdown/text and applies CSS selectors server-side, reducing token usage by 70-90% while providing AI tools with clean, filtered web content.
    7
    MIT

  • markd-mcp

    mayankfulwariya
    A
    license
    Not graded
    quality
    B
    maintenance
    A server that extracts clean Markdown from web pages using headless Chrome, reducing token usage by up to 90% for AI tools like Claude.
    15
    ISC

  • skrape

    AmokOrg
    A
    license
    Not graded
    quality
    D
    maintenance
    Converts any webpage into clean, LLM-ready Markdown, removing noise and supporting JavaScript rendering.
    MIT

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