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JochenYang

Luma MCP

by JochenYang
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Server Quality Checklist

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

  • Latest release: v1.7.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of misselection between tools. The tool's purpose is clearly defined with explicit call conditions and task types.

    Naming Consistency5/5

    The server has a single tool named 'image_understand' in snake_case. With only one tool, there is no naming pattern to conflict; the name is descriptive and consistent with a verb-adjacent structure.

    Tool Count3/5

    The server exposes exactly one tool. While the tool is comprehensive and well-designed, a single tool feels thin for an MCP server per the calibration guidelines, placing it in the borderline category.

    Completeness5/5

    The image_understand tool covers a broad range of use cases (general understanding, OCR, UI analysis, debugging, and description) via its task_type parameter. It provides a complete lifecycle for image understanding, with no obvious missing operations within its stated scope.

  • Average 4.4/5 across 1 of 1 tools scored.

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

    • 5 of 5 community issues answered or closed in the last 6 months
    • 9 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.

  • 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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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?

    No annotations are present, so the description carries the burden of behavioral disclosure. It reveals that the server appends a base vision protocol and optional task guidance, and that auto/omitted task_type uses heuristic behavior ('与旧版行为兼容'). It lists accepted image sources but omits failure modes or async behavior. This is strong context beyond the schema.

    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 a concise bulleted list, front-loaded with the when-to-call section. Every sentence provides actionable information without redundancy. It is well-structured and easy to scan.

    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 no output schema and no siblings, the description covers the key dimensions: when to use, image sources, prompt handling, and task_type options. It does not explicitly state the output format, but the purpose implies a textual analysis. Overall, it is quite complete for the tool's complexity.

    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 detailed parameter descriptions (e.g., prompt: '服务器会补充系统级视觉提示词', task_type: 'ocr=文字提取', image_source: 'data:image/...;base64,...'). The description adds minimal new parameter insight, mostly repeating schema content. Therefore baseline 3 is appropriate.

    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 identifies the tool as an image understanding tool (图像理解工具) with a single entry point. It states specific trigger scenarios ('用户提到看图/截图/界面/报错/OCR/布局') and explicitly differentiates itself as the preferred tool, making its purpose unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Provides explicit when-to-call guidance with enumerated user triggers (查看图片、截图、界面、报错、OCR、布局) and states '优先调用本工具'. It also explains when task_type should be omitted or set to auto for compatibility, giving clear usage context.

    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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