生成图片

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

Annotations already indicate a non-read-only, non-idempotent operation, and the description adds useful behavioral context: it calls a remote Agnes API and supports both URL and base64 output. It does not contradict any annotation, though it could mention side effects like cost or latency.

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?

Two compact sentences with no filler. The main action and model are front-loaded, and the supporting capabilities are listed efficiently.

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 covers the core use case and remote API dependency, and an output schema exists to define return values. Still, with six parameters and no schema-level descriptions, the description leaves gaps around image input format, size meaning, and model choices, making it slightly incomplete for fully confident invocation.

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 description coverage is 0%, so the description must compensate. It partially does by implying prompt for text-to-image, images for reference input, and output for URL/base64. However, it does not clarify size, ratio, or model semantics, leaving the agent to infer from enums and names.

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 states a clear verb and resource: '使用 Agnes 模型生成一张图片' (generate an image with the Agnes model), and adds concrete behavior: text-to-image or reference-image input, returning URL or base64. The singular '一张图片' also helps distinguish from the sibling generate_images.

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 explains what the tool can do but gives no guidance on when to choose it over siblings like generate_images, download_image, or validate_image. There is no mention of when not to use it, prerequisites, or alternative routing.

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