generate_image

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

With no annotations provided, the description carries full responsibility and does so admirably. It discloses that generation happens ONCE, where and how files are saved (out_dir, img-<timestamp>-<i>.png), that enhance auto-expands prompts via ChatGPT, what each style looks like, the effect of thinking levels on text fidelity, and that brand_colors/reserve_corner apply even when enhance=False. This is rich, non-obvious behavioral detail.

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?

Despite being about 200 words, every sentence contributes functional value. The description is front-loaded with the core purpose and critical usage rule, then organizes parameter details into clear, scannable paragraphs. There is no repetition or fluff; it's appropriately dense for the tool's complexity.

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 tool has 9 parameters, no annotations, and no output schema, the description covers the key aspects: return format (JSON with absolute paths), file-saving behavior, generation-once guarantee, and the effects of each parameter. It even mentions edge cases like enhance=False still applying brand_colors. This is very complete for the tool's scope.

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 schema has no descriptions (0% coverage), so the text must compensate. The description explains aspect ratio values, enhance, style meanings, thinking levels, brand_colors, reserve_corner, and out_dir. It implies the 'n' parameter via 'image(s)' and the -<i>.png pattern, but doesn't explicitly state that n controls the count. Still, the explanation covers the vast majority of the 9 parameters with practical meaning.

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 opens with a specific verb+resource statement: 'Generate image(s) from a text prompt at the given aspect ratio.' It also lists the allowed aspect ratios and gives a concrete example of the returned paths. This clearly distinguishes the tool from its only sibling (login_status) and fully conveys its purpose.

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 clear usage context, especially the explicit instruction to use the returned path directly and never re-generate to 'find' the file. It also explains how enhance, style, thinking, brand_colors, and reserve_corner affect output. There are no relevant alternative tools to compare against, so explicit when-not-to-use guidance isn't necessary, but the provided constraints are valuable.

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