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yuzhaoyang001

images-handler

by yuzhaoyang001
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool has a clear and distinct purpose.

    Naming Consistency5/5

    The tool name 'recognize_image' follows a clear verb_noun convention. Although there are no other tools to compare, the naming is internally consistent and predictable.

    Tool Count3/5

    The server has only 1 tool, which feels thin for an 'images-handler' name that implies a broader scope. However, the single tool is non-trivial and provides meaningful functionality, so it is borderline rather than severely inadequate.

    Completeness2/5

    The server only offers image recognition/analysis, missing any other image handling operations like editing, conversion, or resizing. Given the 'images-handler' naming, this is a significant gap that limits the server's usefulness for general image tasks.

  • Average 4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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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

  • Behavior3/5

    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 discloses the use of a vision model and the return type (text description), but it does not mention potential side effects like data transmission to an external service, latency, or failure modes. The disclosure is adequate but not rich.

    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 two sentences, front-loads the primary purpose, and contains no superfluous words. Every phrase contributes to understanding what the tool does and why to use it.

    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?

    For a tool with 3 parameters and no output schema, the description sufficiently covers the purpose and use case. The schema handles parameter details and defaults, so the description does not need to elaborate further. Minor gaps like clarifying multi-image behavior are already inferable from the schema.

    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 coverage is 100% with descriptions for all three parameters (model, images, instruction). The description adds no parameter-specific details beyond what the schema already provides, so it stays at the baseline.

    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 uses specific verbs 'recognize and analyze' and clearly identifies the resource as 'image(s)' and output as 'text description'. It also adds a concrete use case ('give text-only LLMs like DeepSeek image understanding'), making the tool's intent unmistakable.

    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?

    It provides an explicit when-to-use context: 'Use this to give text-only LLMs like DeepSeek image understanding.' However, it does not mention when not to use it or any alternative tools, so it stops short of a full 5.

    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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