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skrapeai

Skrape MCP Server

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Skrape MCP 服务器

铁匠徽章使用skrape.ai将任何网页转换为干净的、可用于 LLM 的 Markdown 格式。非常适合将网页内容输入 LLM。

此 MCP 服务器提供一个简单的界面，可使用 skrape.ai API 将网页转换为结构化、简洁的 Markdown 格式。它旨在与 Claude Desktop、其他 LLM 以及兼容 MCP 的应用程序无缝协作。

为什么使用 Skrape 进行 LLM 集成？

  • 干净、结构化的输出：生成格式良好的 Markdown，非常适合 LLM 使用

  • 降噪：自动删除广告、导航菜单和其他不相关内容

  • 一致的格式：确保网页内容无论来源如何，都具有统一的结构

  • JavaScript 支持：通过在转换之前渲染 JavaScript 来处理动态内容

  • LLM 优化：非常适合将 Web 内容输入到 Claude、GPT 和其他 LLM 模型等 LLM 中

Related MCP server: Scraper MCP

特征

工具

  • get_markdown - 将任何网页转换为 LLM 适用的 Markdown

    • 接受任何输入 URL 和可选参数

    • 返回针对 LLM 使用进行了优化的干净、结构化的 Markdown

    • 支持 JavaScript 渲染动态内容

    • 用于高级集成的可选 JSON 响应格式

安装

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 Skrape MCP 服务器：

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

手动安装

  1. 从skrape.ai获取您的 API 密钥

  2. 安装依赖项：

npm install

  1. 构建服务器：

npm run build

  1. 将服务器配置添加到 Claude Desktop：

在 MacOS 上：

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

在 Windows 上：

notepad %APPDATA%/Claude/claude_desktop_config.json

添加此配置（用您的值替换路径和 API 密钥）：

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

与法学硕士 (LLM) 合作

以下是如何将服务器与 Claude 或其他 LLM 模型一起使用：

  1. 首先，确保你的 LLM 应用程序中的服务器配置正确

  2. 然后，您可以要求 ALLMI 获取并处理任何网页：

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>

生成的 Markdown 将会干净、结构化，并可供 LLM 处理。

高级选项

get_markdown工具接受以下参数：

  • url （必填）：要转换的任何网页 URL

  • returnJson （可选）：设置为true以获取完整的 JSON 响应，而不仅仅是 markdown

  • options （可选）：其他抓取选项

    • renderJs ：是否在抓取之前渲染 JavaScript（默认值：true）

包含所有选项的示例：

<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>

发展

对于使用自动重建的开发：

npm run watch

调试

由于 MCP 服务器通过 stdio 进行通信，调试起来可能比较困难。我们建议使用MCP Inspector ：

npm run inspector

检查器将提供一个 URL 来访问浏览器中的调试工具。

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.
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  • skrape

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