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

Annotations indicate readOnlyHint=false and openWorldHint=true, so the mutating nature is already known. The description adds valuable behavioral context by disclosing asynchronous execution, the returned generation_id, and the required polling step, which are not visible in annotations or 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 tight and efficient: three short sentences cover what it does, pricing, and the key asynchronous polling workflow. Every sentence contributes necessary information, and the main action is front-loaded.

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 description is largely complete for invocation: it explains the asynchronous flow, what is returned (generation_id), and how to get the final result via get_generation. It does not describe potential error states or other status values, but for a simple text-to-image call with a needed follow-up, it is sufficient.

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%, and the schema already documents prompt, model, and aspect_ratio with enums and constraints. The description adds only high-level context like '12 models' and pricing, not meaningful per-parameter semantics beyond 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 'Text to image with 12 models', giving a specific function and resource scope. It is understandable on its own, though it does not explicitly differentiate this tool from the sibling 'generate_image' or other image-generation tools.

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 provides clear usage context by explaining the asynchronous workflow: returns a generation_id and directs the caller to poll get_generation until status is 'completed'. However, it does not give explicit when-to-use versus alternative image tools or state exclusions.

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