Generate image

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses external API key usage, fallback to a labelled mock, and that it returns workspace asset IDs. It does not mention failure modes, cost, or rate limits, but covers the most important behavioral traits.

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 two sentences, front-loaded with the primary action, and every clause adds value: BYO-key, mock fallback, return type, and downstream uses. No filler or repetition.

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?

Given 5 parameters, no output schema, and no annotations, the description covers the core purpose and usage but misses parameter semantics and edge-case behavior. It is adequate for basic invocation but incomplete for complex scenarios like provider selection or error handling.

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 only 40% (label, provider), and the tool description adds no parameter-specific details. It does not explain prompt requirements, n limits, aspect options, or provider semantics, leaving the agent under-informed about how to set parameters.

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 it generates image(s) using the user's API keys, falls back to a mock, and returns workspace asset IDs. This distinguishes it from sibling tools like import_asset or generate_video by specifying exactly what resource it creates and how.

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 indicates when to use the tool by listing downstream consumers (render_video, render_carousel, post drafts). It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to infer appropriate usage.

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