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

Annotations cover safety (readOnly=false, destructive=false) but say nothing about async behavior, cost, or auth. The description adds substantive context beyond them: the multi-minute async model, the cost hold/charge/refund mechanics, the OAuth videos:write consent-scope requirement and reconnect hint, and the idempotency-key deduplication behavior. This is exactly the extra context annotations cannot convey.

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?

Front-loaded with the core purpose and async contract, then constraints, auth, and billing. Every paragraph carries distinct information, but the OAuth-scope aside is somewhat niche for a general audience and the block is longer than strictly necessary.

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 complex, stateful, money-spending tool with no output schema, the description covers everything an agent needs: return value (task id), polling loop, cost semantics, auth prerequisite, and where to find per-model limits. Nothing essential is left to inference.

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 the baseline is 3, but the description adds real meaning: duration/resolution/aspect_ratio values are model-dependent (schema just says "from the values the model supports"), model ids must be vendor-qualified and found via models_search, prompt is text-only, and generate_audio usually costs more. This goes beyond the per-field schema text.

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?

Opens with a specific verb+resource: "Start generating a video from a text prompt," and names the model catalogue it draws from. It clearly differentiates itself from video_get (polling) and models_search (finding model ids), so an agent can route correctly without opening schemas.

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?

Explicitly states the workflow (returns a task id, poll video_get every 15–30s until completed/failed) and draws a firm boundary around what this tool cannot do: image-to-video, editing, upscaling and avatars must go through the HTTP API. It also points to models_get_pricing for per-model constraints. When/when-not and the alternative are both named.

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