Scrapeless MCP Server

스크레이프리스 McP 서버
모델 컨텍스트 프로토콜(MCP)은 LLM 애플리케이션과 외부 데이터 소스 및 도구 간의 원활한 통합을 지원하는 개방형 프로토콜입니다. MCP는 LLM을 필요한 컨텍스트에 연결하는 표준화된 방식을 제공하여 채팅 인터페이스를 효율적으로 개선하고, AI 기반 IDE를 구축하고, 맞춤형 AI 워크플로를 생성하는 데 도움을 줍니다.
Scrapeless MCP 서버를 사용하여 실시간 Google SERP(Google 검색, Google 항공편, Google 지도, Google 채용정보 등) 결과를 LLM 애플리케이션에 원활하게 통합하세요. 이 서버는 ChatGPT, Claude 등의 LLM과 Scrapeless의 Google SERP를 연결하는 다리 역할을 하여 AI 워크플로, 챗봇 및 연구 도구에 대한 동적 컨텍스트 검색을 지원합니다.
👉 라이브 MCP 엔드포인트:
📦 NPM 패키지: scrapeless-mcp-server
개요
이 프로젝트는 Claude와 같은 AI 도우미가 다양한 검색 작업을 수행하고 다음에서 데이터를 검색할 수 있도록 하는 여러 MCP 서버를 제공합니다.
구글 검색
Related MCP server: MCP Web Research Server
도구
1. 검색 도구
이름:
google-search설명: Scrapeless를 사용하여 웹 검색
매개변수:
query(필수): 매개변수는 검색할 쿼리를 정의합니다. 일반 Google 검색에서 사용하는 모든 쿼리를 사용할 수 있습니다. 예: inurl:, site:, intitle:.gl(선택 사항, 기본값: "us"): Google 검색에 사용할 국가를 정의하는 매개변수입니다. 두 글자 국가 코드입니다. (예: 미국은 us, 영국은 uk, 프랑스는 fr)hl(선택 사항, 기본값: "en"): Google 검색에 사용할 언어를 정의하는 매개변수입니다. 두 글자로 된 언어 코드입니다. (예: 영어는 en, 스페인어는 es, 프랑스어는 fr)
설정 가이드
1. 스크레이프리 키 받기
Scrapeless 에 등록하세요
2. 구성
지엑스피1
예제 쿼리
다음은 Claude Desktop과 함께 이러한 서버를 사용하는 방법에 대한 몇 가지 예입니다.
구글 검색
Please search for "climate change solutions" and summarize the top results.설치
필수 조건
Node.js 22 이상
NPM 또는 Yarn
소스에서 설치
저장소를 복제합니다.
git clone https://github.com/scrapeless-ai/scrapeless-mcp-server.git
cd scrapeless-mcp-server종속성 설치:
npm install서버를 빌드하세요:
npm run build지역 사회
Available Tools
1 toolgoogle-searchCInspect
Fetch Google Search Results
| Name | Required | Description | Default |
|---|---|---|---|
| gl | No | Parameter defines the country to use for the Google search. It's a two-letter country code. (e.g., us for the United States, uk for United Kingdom, or fr for France). | |
| hl | No | Parameter defines the language to use for the Google search. It's a two-letter language code. (e.g., en for English, es for Spanish, or fr for French). | |
| query | Yes | Parameter defines the query you want to search. You can use anything that you would use in a regular Google search. e.g. inurl:, site:, intitle:. We also support advanced search query parameters such as as_dt and as_eq. |
TDQS
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. 'Fetch' implies a read operation, but it doesn't disclose critical traits like rate limits, authentication needs, response format, pagination, or error handling. For a search 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just three words, front-loaded and zero waste. Every word ('Fetch Google Search Results') directly contributes to stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (search functionality with parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain return values, error cases, or behavioral constraints, leaving the agent with insufficient information to use the tool effectively beyond basic input.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 three parameters (query, gl, hl) with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as examples or usage tips. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Fetch Google Search Results' states the basic action (fetch) and resource (Google Search Results), but it's vague about scope and format. It doesn't specify what kind of results (e.g., web pages, images, news) or how many results are returned. Without sibling tools, differentiation isn't needed, but the purpose could be more specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. There are no sibling tools mentioned, so no explicit comparisons are needed, but it lacks context about use cases, prerequisites, or limitations. It's a generic statement with no usage instructions.
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 tool update
v1.0.0- First observed
google-search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'google-search' has a clear and distinct purpose of fetching Google search results, so agents cannot misselect between multiple options.
The single tool name 'google-search' follows a consistent pattern of verb-noun (search as the verb, Google as the noun context), and with only one tool, there is no inconsistency to evaluate. The naming is clear and adheres to a predictable structure.
The server has only one tool, which feels thin and under-scoped for a scraping-related domain. A single tool for fetching Google search results may not provide sufficient coverage for typical scraping workflows, such as parsing results, handling pagination, or interacting with other search engines, making it borderline too few for the apparent purpose.
Inferring the domain as web scraping or search data fetching, the tool surface is severely incomplete. It only offers a basic search fetch without supporting operations like filtering results, extracting specific data, managing queries, or integrating with other scraping tasks, leading to significant gaps that could cause agent failures in broader scraping scenarios.
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