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KuaaMU

llm-vision-mcp

by KuaaMU
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Server Quality Checklist

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

  • Latest release: v0.1.8

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The single tool has a clear and distinct purpose: analyzing images.

    Naming Consistency5/5

    The tool name 'analyze_image' follows a clean verb_noun pattern. Since there is only one tool, naming consistency is perfect.

    Tool Count4/5

    The server has exactly one tool, which feels slightly thin but is reasonable for a highly focused vision analysis server. The tool is comprehensive, handling many tasks through parameters, so the count is not inadequate.

    Completeness5/5

    The tool covers a wide range of vision tasks including describe, OCR, UI, layout, and QA, with multiple input sources and output options. There are no obvious gaps for the stated purpose of enabling text-only agents to analyze images.

  • Average 4.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 68 commits in the last 12 weeks
    • 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.

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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

  • Behavior4/5

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

    With no annotations to lean on, the description carries the full burden and does well: it discloses the use of a multimodal model, the text-only nature of the agent, the return of a detailed description, and the effect of 'save_to' (writes to file, returns path + summary). It describes defaults ('detail' defaults to 'high') and source resolution behaviors. It does not cover error cases or permissions, but the disclosed behavior is solid.

    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 well-structured with bullets, front-loads the core purpose in the first sentence, and every subsequent line adds distinct information (sources, tasks, defaults, file output). It is appropriately sized for a tool with 5 parameters and no output schema, with no filler or repetition.

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

    Completeness5/5

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

    Given the tool's complexity (5 parameters, no output schema, no annotations), the description is remarkably complete. It covers all input source types, task modes, prompt override, detail level, and save_to behavior. The only minor omission is exact return formatting, but the description explicitly says 'detailed text description' and 'path + summary' for save_to, which is sufficient for correct invocation.

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

    Parameters5/5

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

    Schema coverage is 100%, so baseline is 3, but the description adds substantial meaning beyond the schema. It explains each image source format with examples, declares the precedence between 'prompt' and 'task', clarifies the 'detail' default, and describes the 'save_to' output behavior. This is exactly the kind of added value that helps an agent pick and fill parameters correctly.

    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 states a specific verb and resource: 'Analyze an image using a multimodal model and return a detailed text description.' It also explains the motivation (the vision model sees the image, the calling agent is text-only), making the purpose unambiguous and strongly differentiated from any generic tool.

    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 clearly explains when to use the tool: whenever a text-only agent needs image understanding. It gives concrete guidance on selecting image sources and tasks, and notes that 'task' is ignored when 'prompt' is provided. No explicit when-not-to-use or alternatives are mentioned, but there are no sibling tools to differentiate from.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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