image_understand

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

With no annotations provided, the description carries the disclosure burden. It reveals a notable behavioral detail: '服务端会自动注入 Focus Hint 和基础视觉协议', explaining automatic server-side processing. However, it omits potential side effects, authentication needs, rate limits, or response format, leaving some behavioral aspects unclear.

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?

The description is a compact paragraph that front-loads the core purpose, then efficiently covers triggers, image sources, usage, and optional task_type. Each sentence contributes useful information without redundancy, though it is slightly dense and could be broken into clearer sections.

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?

The description provides solid context for when to use the tool, image formats, and task_type semantics, which helps an agent select and invoke it. However, since there is no output schema, the description should explain what the tool returns (e.g., text, analysis), but it does not, leaving a significant gap in expected response understanding.

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 coverage is 100%, so the description does not need to repeat parameter details. The description adds minimal extra meaning by clarifying that 'prompt' should be the user's original question and by summarizing task_type's auto-inference behavior, but this largely mirrors the schema's own descriptions.

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?

The description clearly identifies the tool as a '通用图像理解工具' (general image understanding tool) and lists trigger scenarios like viewing images, screenshots, interfaces, errors, and layouts. It effectively communicates the resource and action, but does not explicitly distinguish itself from sibling tools (image_ocr, image_compare), relying on the word '通用' to imply a broader scope.

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?

The description provides explicit when-to-use instructions: '当用户提到看图/截图/界面/报错/布局，或对话中出现图片附件并询问图片相关问题时调用'—covering common triggers and image attachment scenarios. It also advises to '直接传入用户原始问题即可', giving practical usage direction, though it does not mention when not to use it or alternatives.

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