generate_image

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

No annotations are provided, so the description carries full burden. It discloses use of OpenAI models, automatic token/cost calculation, and multiple model capabilities. However, it does not mention overwrite behavior for output_path, rate limits, or authorization needs.

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 two sentences, front-loaded with purpose and model list. No unnecessary words, but the model details could be more structured. Still, it is efficient and clear.

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 tool has no output schema, so the description should explain return values. It mentions optional base64 and thumbnail but does not clarify the default return (presumably saved file path and token usage?). While model details are good, the output behavior is incomplete.

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%, so the schema already describes all parameters. The description adds modest value by summarizing model version differences and auto-calculation of costs, but this information is largely redundant with the enum descriptions in the schema.

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 states the tool generates images from text prompts using specific OpenAI models. It distinguishes itself from siblings like edit_image and transform_image by focusing on generation from scratch.

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 model selection guidance but does not explicitly state when to use this tool versus alternatives like start_generation_job (async vs sync). The model descriptions help choose among models but no explicit when-not or sibling differentiation beyond model choice.

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