generate_openai_image

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

With no annotations, the description carries the full burden of behavioral disclosure, but it only describes the action and model. It does not mention API-key prerequisites, cost or quota implications, external network calls, output format, or side effects such as history storage, even though sibling configuration tools indicate these are relevant.

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 single, front-loaded sentence with no filler, conveying the action, input, and model choice efficiently. It loses a point because it omits routing or behavioral detail that the schema cannot provide.

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 is simple and the schema fully documents its three parameters, making the description minimally viable. However, with no output schema and no mention of prerequisites, cost, or alternatives among the many siblings, it leaves material gaps for an agent deciding whether to call this tool versus generate_image or edit_image.

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 prompt, size, and model are already documented. The description adds no meaningful parameter-level detail beyond restating that the prompt is a text description, so the baseline of 3 applies.

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 specific verb and resource ('Generate a new image from a text description') and adds the model family 'OpenAI DALL-E', which helps distinguish it from video generation and editing tools. It does not explicitly contrast with the sibling generate_image, so it is clear but not fully differentiated.

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 intended use is implied: use this tool when creating a brand-new image from a text prompt rather than editing an existing image. However, there is no explicit when/when-not guidance or naming of alternative tools, leaving selection between this and generate_image mostly to inference.

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