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

Annotations already cover read-only, non-destructive, closed-world operation. The description adds useful behavior beyond that: it returns a modelId, and private:true restricts to the private AI model line (i2v). It does not cover edge cases like no match, but with annotations in place this is solid.

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 one compact sentence plus a parameter note, front-loading the action and return value before listing selection criteria. Every clause is relevant.

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?

No output schema exists, so the description must explain the return value; it does so by stating it returns the modelId for generate_image. With annotations covering safety and schema covering parameters, the definition is complete enough for an agent to call 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?

Schema coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by explaining private:true as restricting to the private AI model line (i2v models) and by summarizing the goal/media/source-image combinations, though it mostly mirrors the enum values.

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 names a specific action (decide/recommend) and resource (model), gives the concrete output (modelId), and ties it to generate_image. It makes clear this is a selection helper, not a generator or lister, so an agent can distinguish it from generate_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?

The description tells when to use the tool: to choose a model for generate_image based on goal (cheapest/fastest/best_quality), media (image/video), and source-image presence. It does not explicitly say when not to use it or name list_models as an alternative, so it stops short of full routing guidance.

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