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.

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

With no annotations provided, the description fully carries the burden. It discloses that the tool is hint-based, performs no network probe, and derives ready_count from persisted hints, making the data's potential staleness transparent. It also details the exact return structure, which is crucial in absence of an output schema.

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 compact yet information-dense. It front-loads the purpose, then adds key behavioral notes and return shape, and finishes with a practical caveat and pointer. Every sentence adds value; there is no filler.

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?

For a zero-parameter tool with no output schema, the description covers all essentials: what it does, its limitations, the exact return value, and when to use an alternative. This is fully self-contained and leaves no critical gaps for an AI agent to invoke it correctly.

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 tool has zero parameters and the schema is trivially covered at 100%. Per the rubric, 0 params earn a baseline of 4. The description adds nothing about parameters because there is nothing to add, but this is appropriate given the tool's design.

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's function: 'Check logged-in ChatGPT accounts.' The verb 'check' and resource 'logged-in ChatGPT accounts' are specific, and the description also outlines the return shape, removing ambiguity. It is easily distinguished from the sibling generate_image tool.

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 explicitly frames when to use this tool: 'Cheap + hint-based (no network probe),' and provides a direct alternative for live data: 'for live quota run the CLI `aigpt accounts`'. This gives the agent a clear decision path between this tool and the recommended alternative.

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