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

The annotations already flag a destructive, non-idempotent, open-world write; the description adds the crucial fact that each call consumes real account credits and how to check cost and balance first. It also discloses model-gated constraints (GIF only on GPT Image 2.6, not on the budget variant, 512 px, text-to-image only). It does not say whether the call is synchronous or returns a job id, which is a meaningful omission.

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 cost warning is correctly front-loaded, but the core purpose sentence is buried in second position behind it. The 21-value model enumeration is a dense block that, while informative, dominates the text and makes the definition read like a schema dump rather than guidance.

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?

With no output schema and 11 parameters, the description does well on inputs, enums, defaults, and credit implications. It leaves the return contract unstated — no mention of whether an image URL comes back directly or whether kreavo_check_image_status must be polled — which is the main gap for an agent wiring this into a workflow.

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 0% across 11 parameters, so the description carries the full burden and largely delivers: it gives the model enum with a stated default, aspect_ratio values, quality values, GIF frame bounds, and which params are GPT Image 2.6-only. The three URL parameters are named but not semantically explained, which is the only shortfall.

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?

"Generate an AI image" states a specific verb and resource, and the opening credit-spend sentence frames the operation's cost. It distinguishes itself from video/edit siblings by naming the output medium, but never explicitly names an alternative (e.g. kreavo_image_edit for modifying an existing image).

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 usefully routes the agent to kreavo_list_models for pricing and kreavo_get_credits for balance before spending, which is real pre-call guidance. However, there is no statement of when to choose this over kreavo_image_edit, kreavo_generate_photoshoot, or kreavo_virtual_tryon, so sibling selection is left to inference.

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