Skip to main content
Glama
kazuph
by kazuph

imageFetch

Extract images from a web page URL, returning them as base64, files, or merged layouts with control over count, size, and quality.

Instructions

画像取得に強いMCPフェッチツール。記事本文をMarkdown化し、ページ内の画像を抽出・最適化して返します。

新APIの既定(imagesを指定した場合)

  • 画像: 取得してBASE64で返却(最大3枚を縦結合した1枚JPEG)

  • 保存: しない(オプトイン)

  • クロスオリジン: 許可(CDN想定)

パラメータ(新API)

  • url: 取得先URL(必須)

  • images: true | { output, layout, maxCount, startIndex, size, originPolicy, saveDir }

    • output: "base64" | "file" | "both"(既定: base64)

    • layout: "merged" | "individual" | "both"(既定: merged)

    • maxCount/startIndex(既定: 3 / 0)

    • size: { maxWidth, maxHeight, quality }(既定: 1000/1600/80)

    • originPolicy: "cross-origin" | "same-origin"(既定: cross-origin)

  • text: { maxLength, startIndex, raw }(既定: 20000/0/false)

  • security: { ignoreRobotsTxt }(既定: false)

旧APIキー(enableFetchImages, returnBase64, saveImages, imageMax*, imageStartIndex 等)は後方互換のため引き続き受け付けます(非推奨)。

Examples(新API) { "url": "https://example.com", "images": true }

{ "url": "https://example.com", "images": { "output": "both", "layout": "both", "maxCount": 4 } }

Examples(旧API互換) { "url": "https://example.com", "enableFetchImages": true, "returnBase64": true, "imageMaxCount": 2 }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
maxLengthNo
startIndexNo
imageStartIndexNo
rawNo
imageMaxCountNo
imageMaxHeightNo
imageMaxWidthNo
imageQualityNo
enableFetchImagesNo
allowCrossOriginImagesNo
ignoreRobotsTxtNo
saveImagesNo
returnBase64No
imagesNo
textNo
securityNo
Behavior4/5

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

With no annotations, the description carries full burden. It details behavioral traits: images are fetched, base64 returned (default), max 3 images merged into one JPEG, no save unless opted in, cross-origin allowed, and old API compatibility. Absent are rate limits or auth needs, but core behavior is transparent.

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

Conciseness3/5

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

The description is lengthy (≈250 words) but well-structured with sections for defaults, parameters, and examples. It is front-loaded with purpose but contains redundant details (e.g., repeating default values in both text and examples). Could be more concise.

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

Completeness4/5

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

Given the tool's complexity (17 parameters, nested objects, no output schema), the description provides extensive detail on new API behavior, defaults, and legacy support. Includes examples. Does not explicitly explain return format beyond base64 and Markdown, but contextually sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so description must compensate. It lists parameters for the new API (images object with output, layout, maxCount, etc.) and mentions old keys. It covers defaults and options, though some top-level schema parameters (e.g., maxLength, startIndex) are explained only under the text object, causing slight ambiguity.

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 it is an MCP fetch tool specialized for image acquisition, converting article text to Markdown and extracting/optimizing images. It specifies verb+resource (fetch and process web pages with images) and no sibling tools exist, so no differentiation needed.

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 explains when to use (for fetching pages with images) and provides detailed parameter behavior for both new and legacy APIs. It does not explicitly exclude scenarios but offers enough context for appropriate use.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kazuph/mcp-fetch'

If you have feedback or need assistance with the MCP directory API, please join our Discord server