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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and a closed-world scope, so the safety profile needs no restating. The description adds real value by disclosing what comes back for each entry (what inputs the model takes and its lowest price in MakeViral credits) and by placing the call in a workflow. It stops short of describing ordering or pagination, so it is not a 5.

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-loads the resource and coverage before the usage triggers, and every sentence contributes scope, triggers, or next steps. The long parenthetical model lists are the one place it drifts toward enumeration for its own sake, slightly inflating length.

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?

Output schema exists, annotations cover the read-only safety profile, and the only parameter is fully described in the schema, so the description's job is chiefly routing and scope. It delivers both, including the credit-pricing detail a caller would otherwise have to discover, leaving nothing essential missing.

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% and the single 'kind' enum parameter is already documented in the schema as 'Only image models or only video models'. The description implies the image/video split through its naming, but never tells the agent that the filter parameter exists or when to set it. Baseline 3 is appropriate since the schema carries the semantics.

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 and resource (list the AI models of MakeViral Create) and enumerates the two model categories with concrete examples. It explicitly contrasts itself with get_model, which handles a single model, so an agent can separate the two without opening sibling 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?

Gives three concrete trigger conditions (user supplies their own idea rather than a trend template, names a specific model like Kling/Veo/Seedance, or asks which model to use) and then names the follow-on tools: get_model for details, create_image/create_video to execute. The when-to-use and what-to-do-next are both explicit.

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