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kazuph

@kazuph/mcp-fetch

by kazuph
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.5.2

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined and distinct.

    Naming Consistency5/5

    The single tool name 'imageFetch' is descriptive and follows a verb_noun pattern. There is no inconsistency since only one tool exists.

    Tool Count4/5

    While the server has only one tool, it is a complex and well-documented tool that handles a wide range of functionality appropriate for its fetch-image purpose. The count is slightly below the typical range but not insufficient.

    Completeness5/5

    The tool comprehensively covers fetching web pages, extracting images, and outputting them in various formats (base64, file, merged, individual). It includes parameters for text extraction and security, leaving no apparent gaps for its stated purpose.

  • Average 4.2/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 issues responded to in the last 6 months
    • No commit activity data available
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
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  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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.

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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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