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Fetch MCP Server

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TypeScript
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MCP 서버 가져오기

MCP 로고 가져오기

이 MCP 서버는 HTML, JSON, 일반 텍스트, 마크다운을 포함한 다양한 형식의 웹 콘텐츠를 가져오는 기능을 제공합니다.

구성 요소

도구

  • 페치_html

    • 웹사이트를 가져와서 HTML로 콘텐츠를 반환합니다.

    • 입력:

      • url (문자열, 필수): 가져올 웹사이트의 URL

      • headers (객체, 선택 사항): 요청에 포함할 사용자 정의 헤더

    • 웹 페이지의 원시 HTML 콘텐츠를 반환합니다.

  • 페치_제이슨

    • URL에서 JSON 파일 가져오기

    • 입력:

      • url (문자열, 필수): 가져올 JSON의 URL

      • headers (객체, 선택 사항): 요청에 포함할 사용자 정의 헤더

    • 구문 분석된 JSON 콘텐츠를 반환합니다.

  • 페치_텍스트

    • 웹사이트를 가져와서 HTML이 아닌 일반 텍스트로 콘텐츠를 반환합니다.

    • 입력:

      • url (문자열, 필수): 가져올 웹사이트의 URL

      • headers (객체, 선택 사항): 요청에 포함할 사용자 정의 헤더

    • HTML 태그, 스크립트 및 스타일이 제거된 웹 페이지의 텍스트 콘텐츠를 반환합니다.

  • 페치_마크다운

    • 웹사이트를 가져와서 Markdown으로 콘텐츠를 반환합니다.

    • 입력:

      • url (문자열, 필수): 가져올 웹사이트의 URL

      • headers (객체, 선택 사항): 요청에 포함할 사용자 정의 헤더

    • 웹페이지의 내용을 마크다운 포맷으로 변환하여 반환합니다.

자원

이 서버는 영구적인 리소스를 제공하지 않습니다. 필요에 따라 웹 콘텐츠를 가져와서 변환하도록 설계되었습니다.

Related MCP server: Fetch MCP Server

시작하기

  1. 저장소를 복제합니다

  2. 종속성 설치: npm install

  3. 서버 빌드: npm run build

용법

서버를 사용하려면 다음과 같이 직접 실행하면 됩니다.

지엑스피1

이렇게 하면 stdio에서 Fetch MCP 서버가 실행됩니다.

데스크톱 앱 사용

이 서버를 데스크톱 앱과 통합하려면 앱의 서버 구성에 다음을 추가하세요.

{
  "mcpServers": {
    "fetch": {
      "command": "node",
      "args": [
        "{ABSOLUTE PATH TO FILE HERE}/dist/index.js"
      ]
    }
  }
}

특징

  • 최신 Fetch API를 사용하여 웹 콘텐츠를 가져옵니다.

  • 요청에 대한 사용자 정의 헤더를 지원합니다.

  • HTML, JSON, 일반 텍스트, 마크다운 등 다양한 형식으로 콘텐츠를 제공합니다.

  • HTML 구문 분석 및 텍스트 추출을 위해 JSDOM을 사용합니다.

  • HTML을 Markdown으로 변환하기 위해 TurndownService를 사용합니다.

개발

  • npm run dev 실행하여 감시 모드에서 TypeScript 컴파일러를 시작합니다.

  • npm test 사용하여 테스트 모음을 실행하세요.

특허

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다.

Available Tools

4 tools
fetch_htmlC

Fetch a website and return its unmodified contents as HTML

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the website to fetch
headersNoOptional headers to include in the request
max_lengthNoMaximum number of characters to return (default: 5000)
start_indexNoStart content from this character index (default: 0)

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 that content is 'unmodified' and returned 'as HTML', which is helpful, but lacks critical details like error handling, timeout behavior, authentication needs, rate limits, or whether it follows redirects. For a fetch operation with potential network issues, 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 that front-loads the core functionality ('fetch a website') and specifies the output ('unmodified contents as HTML'). Every word earns its place with zero waste or redundancy.

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 (network operation with 4 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't address error cases, performance characteristics, or what happens with malformed HTML. For a fetch tool that could encounter various real-world issues, more context is needed.

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 meaningful parameter semantics beyond what's in the schema (e.g., it doesn't explain how 'max_length' interacts with HTML structure or whether 'headers' can override defaults). 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 with a specific verb ('fetch') and resource ('website'), and indicates the output format ('HTML'). It distinguishes from sibling tools by specifying 'unmodified contents as HTML' rather than JSON, markdown, or text formats. However, it doesn't explicitly contrast with siblings beyond the output format.

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_json, fetch_markdown, or fetch_txt. It doesn't mention scenarios where HTML output is preferred over other formats, nor does it discuss any prerequisites or constraints for usage.

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

fetch_jsonC

Fetch a JSON file from a URL

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the JSON to fetch
headersNoOptional headers to include in the request
max_lengthNoMaximum number of characters to return (default: 5000)
start_indexNoStart content from this character index (default: 0)

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 states the basic action but omits critical details such as error handling (e.g., for invalid URLs or non-JSON responses), authentication needs, rate limits, or whether it performs safe read operations. This leaves significant gaps in understanding the tool's behavior.

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, direct sentence that efficiently conveys the core purpose without any wasted words. It is front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.

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 moderate complexity (4 parameters, no output schema, and no annotations), the description is incomplete. It lacks details on return values, error cases, or behavioral traits, which are essential for an agent to use the tool effectively in varied contexts.

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 all four parameters (url, headers, max_length, start_index). The description adds no additional meaning beyond what the schema provides, such as examples or usage notes, so it meets the baseline for adequate but unenhanced parameter semantics.

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 action ('Fetch') and resource ('a JSON file from a URL'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like fetch_html or fetch_markdown, which likely perform similar operations on different file types, so it misses full sibling distinction.

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_html or fetch_markdown. It lacks any mention of prerequisites, exclusions, or specific contexts, leaving the agent to infer usage based on the tool name alone.

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

fetch_markdownC

Fetch a website and return its contents converted content to Markdown

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the website to fetch
headersNoOptional headers to include in the request
max_lengthNoMaximum number of characters to return (default: 5000})
start_indexNoStart content from this character index (default: 0)

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 fetches and converts content, but lacks details on error handling, rate limits, authentication needs, or what happens with invalid URLs. For a web-fetching tool with zero 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.

Conciseness5/5

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

The description is a single, efficient sentence: 'Fetch a website and return its contents converted content to Markdown.' It's front-loaded with the core purpose, has zero wasted words, 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.

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 (web fetching with conversion), lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like error cases, performance, or output format details. For a tool that interacts with external resources and transforms data, more context is needed.

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 fully documents all four parameters (url, headers, max_length, start_index). The description adds no additional parameter semantics beyond what's in the schema, such as format examples or constraints. 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: 'Fetch a website and return its contents converted content to Markdown.' It specifies the verb ('fetch'), resource ('website'), and transformation ('converted to Markdown'). However, it doesn't explicitly differentiate from sibling tools like fetch_html, fetch_json, and fetch_txt, which presumably return different formats.

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 sibling tools or contexts where Markdown output is preferred over HTML, JSON, or plain text. Usage is implied by the tool's name and purpose, but no explicit when/when-not instructions are given.

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

fetch_txtA

Fetch a website, convert the content to plain text (no HTML)

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the website to fetch
headersNoOptional headers to include in the request
max_lengthNoMaximum number of characters to return (default: 5000)
start_indexNoStart content from this character index (default: 0)

TDQS

A3.7/5.0
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. It mentions fetching and conversion but lacks details on error handling, rate limits, authentication needs, or what happens with invalid URLs. For a tool with no annotations, this leaves significant behavioral gaps.

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 functionality with zero wasted words, making it easy to understand quickly.

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 no annotations and no output schema, the description covers the basic purpose but lacks details on return values, error cases, or operational constraints. It is minimally adequate but has clear gaps for a tool that interacts with external websites.

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 fully documents all parameters. The description does not add any parameter-specific details beyond what the schema provides, such as examples or usage tips, meeting the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('fetch a website') and transformation ('convert the content to plain text (no HTML)'), distinguishing it from sibling tools like fetch_html, fetch_json, and fetch_markdown which handle different output formats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly suggests usage for extracting plain text from websites, but does not explicitly state when to use this tool versus alternatives like fetch_html for raw HTML or fetch_markdown for markdown conversion. It provides clear context but lacks explicit exclusions or named alternatives.

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. 1 tool updatev1.0.0
    • Changedfetch_markdown1 field changed
      • changedInput schema / properties / max_length / description
        Previous value: -"Maximum number of characters to return (default: 5000)"New value: +"Maximum number of characters to return (default: 5000})"
  2. 4 tool updates
    • First observedfetch_html
    • First observedfetch_json
    • First observedfetch_markdown
    • First observedfetch_txt
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.6/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: fetch_html returns raw HTML, fetch_json retrieves JSON files, fetch_markdown converts to Markdown, and fetch_txt extracts plain text. There is no ambiguity or overlap in functionality, making it easy for an agent to select the correct tool based on the desired output format.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with 'fetch_' prefix and descriptive suffixes (html, json, markdown, txt). This predictable naming scheme enhances readability and usability, allowing agents to intuitively understand each tool's function without confusion.

Tool Count5/5

With 4 tools, the server is well-scoped for its purpose of fetching and converting web content. Each tool earns its place by covering distinct output formats (HTML, JSON, Markdown, plain text), avoiding bloat while providing comprehensive coverage for common web data retrieval needs.

Completeness5/5

The tool set provides complete coverage for the domain of fetching web content in various formats. It includes raw HTML, structured JSON, and converted formats (Markdown and plain text), leaving no obvious gaps. Agents can handle a wide range of web data retrieval scenarios without dead ends.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Related MCP Connectors

Related MCP Servers

  • Fetch MCP Server

    goswamig
    A
    license
    B
    quality
    D
    maintenance
    Provides functionality to fetch web content in various formats, including HTML, JSON, plain text, and Markdown with support for custom headers.
    4
    73,147 npm
    3
    MIT

  • Fetch MCP Server

    phpmac
    A
    license
    Not graded
    quality
    C
    maintenance
    Provides functionality to fetch and transform web content in various formats (HTML, JSON, plain text, and Markdown) through simple API calls.
    73,147 npm
    1
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  • Fetch MCP Server

    SolonaBot
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables fetching and converting web content into various formats including HTML, JSON, plain text, and Markdown. It supports custom request headers and provides specialized tools for on-demand web data retrieval and transformation.
    73,147 npm
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