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

Annotations already indicate non-read-only, non-destructive, and open-world behavior. The description adds valuable context beyond annotations: the specific external model (Black Forest Labs), the photorealism focus, and the cost (~$0.08), which helps the agent anticipate side effects and the need for payment.

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 that conveys the core purpose, model, style, and cost without any wasted words. Every element earns its place.

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 simple 2-parameter tool with an output schema, the description covers the essential context: what it does, which model, and costs. It does not mention latency or any prompt constraints, but the output schema and annotations handle return values and safety, so this 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%: 'prompt' is described as 'What to draw' and 'aspect_ratio' lists all valid values with a default. The tool description adds no additional parameter semantics, so baseline 3 is appropriate.

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 action ('Generate an AI image') and specifies the model (Flux 2) and provider (Black Forest Labs) with a distinctive style tag ('photorealism'). This distinguishes it from sibling tools like ai_image, ai_image_gpt, and ai_image_pro, which target different models or performance levels.

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 implies when to use this tool (when a photorealistic image is desired, specifically via Flux 2) and mentions a cost factor (~$0.08). However, it does not explicitly compare alternatives or state when not to use it, so it falls short of a full 5.

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