ui_parse

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

无 annotations，描述承担披露责任。它明确说明了 YOLO 检测器在 models/icon_detect.pt 存在时自动启用、输出带 id 的结构化元素列表、out_path 保存半透明叠加层编号图等行为信息，这些都是超出工具名的实际执行细节。但未提及是否只读、失败行为或输出字段细节，因此未到满分。

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

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

一段长句用分号分隔，信息密度高，主要功能前置，每部分都有必要信息。但语句较长，未采用分点或短句组织，可读性略低于极简两句话的示例，属于高效但不够精致。

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

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

工具较复杂（OCR+控件检测+图标+可选 YOLO+输出文件），且无 output schema。描述说明了输出为带 id 的结构化元素列表、叠加层图保存的核心用途、YOLO 自动启用条件，足以让 agent 理解如何调用。但未提供元素字段结构或错误场景，完整度略低于满分。

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

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

schema 已覆盖 coords 和 out_path 并提供说明，image 参数无 schema 描述。描述补充了 image 应为全屏 UI 截图，并解释了 out_path 的叠加层编号图用途，但未说明 image 的具体格式或元素列表字段结构。在 67% schema 覆盖基础上，描述增加的语义有限，符合基线 3。

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

描述以“全屏 UI 结构化解析”开头，明确动词和资源，并列举 OCR 文本块、button/input 矩形检测、图标候选、可选 YOLO 检测器，输出带 id 的结构化元素列表。这与 ocr_image、locate_object 等兄弟工具形成清晰区分，功能定位具体且明确。

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

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

描述提供了核心适用场景（全屏 UI 解析、输出编号元素供 VLM 选择）以及可选 YOLO 自动启用的条件，但未明确说明何时不使用本工具或与 ui_locate、ui_refine 等替代工具的取舍。使用场景主要是隐含的，缺少显式排除或替代建议。

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