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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with others. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    The single tool name follows a consistent verb_noun pattern (analyze_image), which is clear and indicative of its function.

    Tool Count4/5

    A single tool is below the typical 3-15 range, but it is reasonable for a narrowly-focused image analysis server. It does not feel excessive or overly sparse given the server's specific purpose.

    Completeness5/5

    The tool covers all necessary input types (local, base64, URL) and a wide range of analysis capabilities (text, objects, scenes, charts), leaving no obvious gaps for the stated purpose of image analysis.

  • Average 3.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 5 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
  • 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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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

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

  • Behavior3/5

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

    无注解,描述补充了输入格式和分析类型,但未说明返回的具体内容、模型选择(如 qwen-vl-plus vs qwen-vl-max)对结果的影响,也未提及三个图片来源必须互斥这一行为。描述对行为透明度的贡献一般。

    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?

    描述仅两句话,第一句直接点明核心功能,第二句补充输入格式与用途,没有冗余信息,结构紧凑且前置信息充分。

    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?

    工具结构简单,参数 schema 覆盖完整,但描述未提及必须提供一种图片来源、未说明返回值或输出形式,且无注解补充,对于无输出 schema 的工具略显不完整。

    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 描述覆盖率 100%,每个参数都有详细说明,描述本身没有额外解读参数,也没有需要补偿的缺口,故按基线 3 分评分。

    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?

    描述明确说明使用多模态模型分析图片内容,动作和对象具体,并列出支持的文件路径、Base64、URL 三种输入方式及应用场景。尽管没有兄弟工具需要区分,但功能定位已经足够清晰。

    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?

    描述清晰说明了适用于分析图片内容、识别文字/物体/场景/图表等场景,并列举了输入格式,提供了使用上下文。虽然没有明确排除场景或给出替代工具,但无兄弟工具时已算清晰。

    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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