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

No annotations are provided, so the description carries the full burden. It does contribute two behavioral facts beyond the schema: prices are 'live' (fresh, not cached) and quoted per image or per video clip. It does not state that the operation is read-only, whether any auth tier is needed, or how the result is shaped.

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?

One short sentence with no filler, and the most important qualifier ('Live') is front-loaded. It is slightly fragmentary rather than a complete verb-led statement, but nothing is wasted.

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 one-parameter read tool this covers purpose adequately, but with no output schema and no annotations the agent gets no view of the returned fields (currency, per-model entries, units). Mildly incomplete rather than severely deficient.

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 100% and the single parameter already documents 'Model id; omit for all models'. The description restates the same one-or-all semantics without adding format examples or behavior for an unknown model id, so baseline 3 applies.

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 the resource and the unit of measure precisely: 'Live price per image or per video clip' for one model or all. An agent can distinguish it from run_model or get_model_schema by the pricing resource, though the verb ('get'/'look up') is only implied rather than stated.

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?

No statement of when to call this versus siblings like search_models or get_model_schema, and no preconditions. The only usage hint is the embedded 'omit for all models', which is parameter guidance rather than tool selection guidance.

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