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Skrape MCP Server

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스크레이프 MCP 서버

대장간 배지 skrape.ai를 사용하여 모든 웹페이지를 깔끔하고 LLM에 적합한 마크다운으로 변환하세요. 웹 콘텐츠를 LLM에 제공하기에 적합합니다.

이 MCP 서버는 skrape.ai API를 사용하여 웹 페이지를 구조화되고 깔끔한 마크다운 형식으로 변환하는 간편한 인터페이스를 제공합니다. Claude Desktop, 다른 LLM 및 MCP 호환 애플리케이션과 원활하게 작동하도록 설계되었습니다.

LLM 통합을 위해 Skrape를 사용하는 이유는 무엇입니까?

  • 깔끔하고 체계적인 출력 : LLM 사용에 적합한 잘 포맷된 마크다운을 생성합니다.

  • 노이즈 감소 : 광고, 탐색 메뉴 및 기타 관련 없는 콘텐츠를 자동으로 제거합니다.

  • 일관된 형식 : 소스에 관계없이 웹 콘텐츠가 균일하게 구성되도록 보장합니다.

  • JavaScript 지원 : 변환 전에 JavaScript를 렌더링하여 동적 콘텐츠를 처리합니다.

  • LLM 최적화 : Claude, GPT 및 기타 LLM 모델과 같은 LLM에 웹 콘텐츠를 공급하는 데 적합합니다.

Related MCP server: Scraper MCP

특징

도구

  • get_markdown - 모든 웹페이지를 LLM 지원 마크다운으로 변환

    • 모든 입력 URL과 선택적 매개변수를 사용합니다.

    • LLM 소비에 최적화된 깔끔하고 체계적인 마크다운을 반환합니다.

    • 동적 콘텐츠에 대한 JavaScript 렌더링을 지원합니다.

    • 고급 통합을 위한 선택적 JSON 응답 형식

설치

Smithery를 통해 설치

Smithery를 통해 Claude Desktop용 Skrape MCP 서버를 자동으로 설치하려면:

지엑스피1

수동 설치

  1. skrape.ai 에서 API 키를 받으세요

  2. 종속성 설치:

npm install

  1. 서버를 빌드하세요:

npm run build

  1. Claude Desktop에 서버 구성을 추가합니다.

MacOS의 경우:

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

Windows의 경우:

notepad %APPDATA%/Claude/claude_desktop_config.json

다음 구성을 추가합니다(경로와 API 키를 해당 값으로 바꿉니다).

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

LLM과 함께 사용

Claude 또는 다른 LLM 모델과 함께 서버를 사용하는 방법은 다음과 같습니다.

  1. 먼저 LLM 애플리케이션에서 서버가 올바르게 구성되었는지 확인하세요.

  2. 그런 다음 ALLMI에 웹페이지를 가져와 처리하도록 요청할 수 있습니다.

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>

그 결과로 나온 마크다운은 깔끔하고 체계적이며 LLM 처리에 적합합니다.

고급 옵션

get_markdown 도구는 다음 매개변수를 허용합니다.

  • url (필수): 변환할 웹페이지 URL

  • returnJson (선택 사항): 마크다운만 받는 대신 전체 JSON 응답을 받으려면 true 로 설정합니다.

  • options (선택 사항): 추가 스크래핑 옵션

    • renderJs : 스크래핑 전에 JavaScript를 렌더링할지 여부(기본값: true)

모든 옵션이 포함된 예:

<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>

개발

자동 재빌드를 사용한 개발의 경우:

npm run watch

디버깅

MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. MCP Inspector 사용을 권장합니다.

npm run inspector

검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.

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
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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

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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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