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

The description discloses important non-obvious behaviors: it returns a job_id rather than the image directly, requires polling check_job, takes about a minute, is hosted via URL, is rate-limited, and currently has zero cost. The annotations are all false, so no contradiction exists, and the description adds substantial behavioral context beyond them.

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?

Three dense sentences cover the core transformation, use cases, async behavior, cost, and rate limits without filler. The most important information is front-loaded and every sentence carries value.

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 the simple two-parameter schema, the presence of an output schema, and detailed async/rate-limit/cost context, the description fully equips an agent to call the tool correctly and know how to retrieve the result. Nothing essential is missing.

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 100%, with the schema already explaining prompt and aspect ratio. The description adds a general example of what to include in the prompt but does not go beyond the schema's parameter documentation. Baseline 3 is appropriate since structured fields carry the weight.

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 clear transformation arrow ('Text prompt → a generated image') and specifies concrete use cases like illustrations, covers, and concept art. It clearly differentiates from siblings such as make_chart and render_diagram by focusing on image generation from prompts.

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 names when to use the tool ('Use it for illustrations, covers, concept art, social cards and mockups') and signals the asynchronous workflow by directing the agent to poll check_job. It does not enumerate exclusions or alternatives, but the use-case list is concrete enough for most selections.

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