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superboolgithub

nanobanana-mcp

by superboolgithub
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: generate_image creates from a text prompt, while edit_image uses a reference image. Their purposes do not overlap, and the parameter differences (prompt + aspectRatio vs. prompt + referenceImageUrl) make selection unambiguous.

    Naming Consistency5/5

    Both tools follow the exact same verb_noun pattern with underscores: generate_image and edit_image. This is perfectly consistent and predictable.

    Tool Count3/5

    With only 2 tools, the server feels thin for a general image generation service, but it is tightly scoped to the two core generation workflows. It is borderline but not unreasonable for a specialized server.

    Completeness4/5

    The server covers the primary image generation operations: text-to-image and image-to-image editing. Minor gaps exist (e.g., no upscaling or variation tools), but for the stated purpose, the surface is mostly complete.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • Behavior2/5

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

    没有提供 annotations，描述承担了全部安全/行为披露责任。但描述仅说明'生成新图片'这一动作，未提及权限要求、速率限制、输出格式或潜在副作用，行为透明性不足。

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    描述以一句话概述用途，随后用列表方式呈现参数，结构清晰，没有冗余内容。虽然参数列表与 schema 重复，但作为面向代理的自然语言说明是可接受的。

    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?

    工具本身较简单，schema 完整覆盖参数，但缺少与兄弟工具的使用边界和返回值说明。在无 annotations 和 output schema 的情况下，描述仅达到最低可用标准，未提供更丰富的上下文。

    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 描述覆盖率达 100%，描述中的参数说明与 schema 属性描述基本重复，未增加额外语义。'必填'标注在 schema 中已有体现，因此描述未提供超越 schema 的价值，维持基线 3 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?

    描述以'基于参考图片生成新图片（图生图）'开头，明确说明了工具的核心功能：基于参考图片生成新图片。动词'生成'和资源'图片'清晰，并且'基于参考图片'与兄弟工具 generate_image（文生图）形成明显区分。

    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?

    描述明确指出了图生图的使用场景，让 agent 能根据是否有参考图片来决定选择此工具而非 generate_image。但未显式给出'何时不使用'或指名替代工具，因此未达到最高分。

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided, so the description must carry the behavioral burden. It adds 'OpenAI 兼容协议' and default values for parameters, which is useful context. However, it does not disclose the return format, whether the operation is synchronous, or any side effects beyond generation.

    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 concise and front-loaded with the purpose, followed by a clear bulleted parameter list. Every sentence provides actionable information without fluff or repetition.

    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?

    All parameters are documented with defaults and enums, but the description omits the output format (e.g., image URL, base64) and lacks usage guidance relative to the sibling tool. For a generation tool with no output schema or annotations, this is a notable gap but still usable for parameter selection.

    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 100%, so the baseline is 3. The description adds value by explicitly marking 'prompt' as required, listing all enum values for aspectRatio and imageSize, and providing default values for aspectRatio, imageSize, and model, which are not present in the schema descriptions.

    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 '生成 AI 图片' (generate AI images) with the OpenAI-compatible protocol, giving a specific verb and resource. It distinguishes from the sibling tool 'edit_image' by explicitly focusing on generation rather than editing.

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

    Usage Guidelines3/5

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

    Usage is implied from the tool name and description (use for generating images vs editing), but there is no explicit guidance on when to prefer this tool over alternatives. No exclusions or alternative tool references are provided.

    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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