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serper-search-scrape-mcp-server

by marcopesani
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TypeScript
Remote

Serper Search 및 MCP 서버 스크래핑

대장간 배지

Serper API를 사용하여 웹 검색 및 웹페이지 스크래핑 기능을 제공하는 TypeScript 기반 MCP 서버입니다. 이 서버는 Claude Desktop과 통합되어 강력한 웹 검색 및 콘텐츠 추출 기능을 제공합니다.

특징

도구

  • google_search - Serper API를 통해 웹 검색 수행

    • 유기적 검색 결과, 지식 그래프, "사람들이 묻는 질문" 및 관련 검색을 포함한 풍부한 검색 결과

    • 지역 및 언어 타겟팅 지원

    • 위치, 페이지 매김, 시간 필터 및 자동 수정에 대한 선택적 매개변수

    • 고급 검색 연산자를 지원합니다:

      • site : 특정 도메인으로 결과 제한

      • filetype : 특정 파일 유형으로 제한(예: 'pdf', 'doc')

      • inurl : URL에 단어가 포함된 페이지 검색

      • intitle : 제목에 단어가 포함된 페이지 검색

      • related : 유사한 웹사이트 찾기

      • cache : 특정 URL의 Google 캐시된 버전을 봅니다.

      • before : YYYY-MM-DD 형식의 이전 날짜

      • after : YYYY-MM-DD 형식의 이후 날짜

      • exact : 정확한 구문 일치

      • exclude : 검색 결과에서 제외할 용어

      • or : 대체 용어(OR 연산자)

  • scrape - 웹 페이지에서 콘텐츠 추출

    • 일반 텍스트와 선택적 마크다운 콘텐츠를 받으세요

    • JSON-LD 및 헤드 메타데이터 포함

    • 문서 구조를 유지합니다

Related MCP server: go_serper_mcp_server

요구 사항

  • 노드.js >= 18

  • Serper API 키( SERPER_API_KEY 환경 변수로 설정)

개발

종속성 설치:

지엑스피1

서버를 빌드하세요:

npm run build

자동 재빌드를 사용한 개발의 경우:

npm run watch

테스트 실행:

npm test                  # Run all tests
npm run test:watch       # Run tests in watch mode
npm run test:coverage    # Run tests with coverage
npm run test:integration # Run integration tests

환경 변수

루트 디렉토리에 .env 파일을 만듭니다.

SERPER_API_KEY=your_api_key_here

디버깅

MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.

npm run inspector

검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.

설치

Smithery를 통해 설치

Smithery를 통해 Claude Desktop용 Serper Search and Scrape를 자동으로 설치하려면:

npx -y @smithery/cli install @marcopesani/mcp-server-serper --client claude

클로드 데스크탑

다음 위치에 서버 구성을 추가합니다.

  • MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • 윈도우: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "serper-search": {
      "command": "npx",
      "args": ["-y", "serper-search-scrape-mcp-server"],
      "env": {
        "SERPER_API_KEY": "your_api_key_here"
      }
    }
  }
}

클라인

  1. Cline 확장 프로그램 설정을 엽니다.

  2. "MCP 서버" 탭을 엽니다.

  3. "MCP 서버 구성"을 클릭하세요.

  4. 서버 구성을 추가합니다.

{
  "mcpServers": {
    "github.com/marcopesani/mcp-server-serper": {
      "command": "npx",
      "args": ["-y", "serper-search-scrape-mcp-server"],
      "env": {
        "SERPER_API_KEY": "your_api_key_here"
      },
      "disabled": false,
      "autoApprove": ["google_search", "scrape"]
    }
  }
}

추가 Cline 구성 옵션:

  • disabled : 서버를 활성화하려면 false 로 설정합니다.

  • autoApprove : 각 사용에 대해 명시적 승인이 필요하지 않은 도구 목록

커서

  1. 커서 설정을 엽니다

  2. "기능" 설정 열기

  3. "MCP 서버" 섹션에서 "새 MCP 서버 추가"를 클릭합니다.

  4. 이름을 선택하고 "유형"으로 "명령"을 선택하세요.

  5. "명령" 필드에 다음을 입력하세요.

env SERPER_API_KEY=your_api_key_here npx -y serper-search-scrape-mcp-server

도커

Docker를 사용하여 서버를 실행할 수도 있습니다. 먼저 이미지를 빌드하세요.

docker build -t mcp-server-serper .

그런 다음 Serper API 키로 컨테이너를 실행합니다.

docker run -e SERPER_API_KEY=your_api_key_here mcp-server-serper

또는 .env 파일에 환경 변수가 있는 경우:

docker run --env-file .env mcp-server-serper

개발을 위해 소스 코드를 볼륨으로 마운트할 수 있습니다.

docker run -v $(pwd):/app --env-file .env mcp-server-serper

참고: your_api_key_here 실제 Serper API 키로 바꿔야 합니다.

Available Tools

2 tools
scrapeC

Tool to scrape a webpage and retrieve the text and, optionally, the markdown content. It will retrieve also the JSON-LD metadata and the head metadata.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL of the webpage to scrape.
includeMarkdownNoWhether to include markdown content.

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what the tool retrieves (text, markdown, JSON-LD, head metadata) but lacks critical behavioral details such as rate limits, authentication needs, error handling, or whether it performs destructive actions (e.g., modifying data). For a web scraping tool with no annotation coverage, this is a significant gap in transparency.

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

Conciseness4/5

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

The description is concise and front-loaded in a single sentence, efficiently stating the core functionality without unnecessary details. Every part earns its place, though it could be slightly more structured (e.g., separating outputs). It avoids redundancy and is appropriately sized for the tool's complexity.

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

Completeness3/5

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

Given the tool's moderate complexity (web scraping with two parameters), no annotations, and no output schema, the description is partially complete. It covers what the tool retrieves but misses behavioral aspects like rate limits or error handling. Without an output schema, it should ideally describe return values more explicitly, but it does list the types of content retrieved, providing some context.

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?

The input schema has 100% description coverage, clearly documenting both parameters ('url' and 'includeMarkdown'). The description adds no additional meaning beyond the schema—it doesn't explain parameter interactions, constraints, or usage examples. With high schema coverage, the baseline score of 3 is appropriate as the schema handles 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: 'scrape a webpage and retrieve the text and, optionally, the markdown content. It will retrieve also the JSON-LD metadata and the head metadata.' This specifies the verb (scrape), resource (webpage), and outputs (text, markdown, JSON-LD, head metadata). However, it doesn't explicitly differentiate from the sibling tool 'google_search', which likely serves a different purpose (searching vs. scraping).

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. It doesn't mention the sibling tool 'google_search' or any other scraping-related tools, nor does it specify prerequisites, contexts, or exclusions for usage. This leaves the agent without clear direction on tool selection.

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.

  1. 2 tool updatesv1.0.0
    • First observedgoogle_search
    • First observedscrape
Deploy Server
Give agents eyes on the webHosted screenshot MCP. Send a URL, get a pixel-perfect image. $0.002 per grab. Works with Claude, Cursor, and Codex.grabbit.liveAd

TDQS

B3.3/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: google_search is for performing web searches and retrieving search results, while scrape is for extracting content from a specific webpage. There is no overlap in functionality, making it easy for an agent to choose the right tool for each task.

Naming Consistency5/5

Both tools follow a consistent naming pattern using snake_case with descriptive, action-oriented names (google_search and scrape). The naming is straightforward and predictable, with no deviations or mixed conventions.

Tool Count3/5

With only 2 tools, the server feels thin for a search-and-scrape domain, as it might lack additional utilities like batch processing, advanced filtering, or result summarization. However, the core functions are covered, making it borderline but functional.

Completeness4/5

The tools cover the essential workflows for search and scraping: performing searches and extracting webpage content. Minor gaps exist, such as no tool for managing search history or handling pagination, but agents can work around these with the provided tools.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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