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

Annotations already declare the unsafe, costly profile (destructiveHint=true, readOnlyHint=false, idempotentHint=false), and the description adds genuinely new behavior: the call is ASYNC, returns a jobId, requires polling get_job, and spends credits. The credit warning is the key non-obvious cost signal. It stops short of describing failure modes or credit amounts.

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?

Three compact fragments, front-loaded with the verb and model, then the async/cost contract, then the mode options. No filler sentences and nothing buried.

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 an async, credit-spending five-parameter tool with no output schema and no sibling differentiation, the description covers the lifecycle and modes but omits several parameter semantics and any distinction from neighboring image-generation tools. Adequate but with visible gaps.

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%, so the description must carry parameter meaning. It defines the three mode enum values and their outputs (single, 4-beat storyboard grid, character sheet) and flags image-single as default, but says nothing about size, count, or aspectRatio, leaving three parameters entirely unexplained.

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?

States a specific verb (generate) and resource (image) with the underlying model named, plus the async return contract. It does not differentiate itself from the sibling studio_generate_image, which is the main gap.

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 tells the agent what to do after calling (poll get_job for imageUrls) and explains the three modes, which is real guidance. However, it never says when to pick this tool over the sibling image generators (studio_generate_image, generate_meme, generate_influencer_studio_image), leaving that inference to the agent.

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