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

With no annotations, the description carries the full burden. It discloses that results are returned synchronously ('同步返回') and enumerates what is returned (value, aspect ratios, prompt/reference limits), but it does not explicitly state read-only nature, permission requirements, or any side effects.

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 three short sentences, front-loading the purpose and usage before return details. Every sentence adds value with no redundancy.

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?

Given zero parameters and no output schema, the description compensates by listing the returned fields and noting synchronous return. It is nearly complete for a simple list tool, though it omits auth or pagination details (likely unnecessary).

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?

The tool takes zero parameters, so the baseline score of 4 applies. The description does not need to add input parameter semantics, and it correctly focuses on output details.

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 uses a specific verb '列出' (list) and resource 'AI 生图模型' (AI image generation models), scoped to '当前机构可用' (available to current organization). It also names the sibling consumer 'ai_generate_image' for model selection, which helps distinguish it from other list tools.

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?

It explicitly states the tool is for 'ai_generate_image 选 model' (ai_generate_image to select model), providing clear usage context. However, it does not state when not to use it or list alternative tools, so it lacks exclusions.

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