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

Beyond the readOnlyHint annotation, it discloses important behaviors: only live generation-enabled modes are executable, required references must be private MaxVideoAI assets, aspectRatio and audio settings have specific omission rules, and empty promptingSources should be reported as absent rather than invented. These are practical behavioral traits an agent cannot infer from annotations or the simple input schema.

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 opening sentence is strong and front-loaded, but the description becomes a dense single-paragraph dump of mode semantics, asset rules, and generation guidance. Much of this is valuable, but the t2v/ref2v/v2v taxonomy could be condensed or moved to an output schema or separate reference; it is more verbose 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?

With no output schema, the description carries the burden of explaining return-value semantics, and it does so extensively: mode classification, asset requirements, aspect ratio and audio handling, promptingSource behavior, and execution boundaries. It lacks precise detail on what 'evidence' covers, but overall it gives an agent enough context to invoke the tool and interpret common results.

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?

The single `id` parameter is already fully documented in the schema as 'Exact public MaxVideoAI model ID returned by list_models or recommend_models'. The description adds 'one known public' but does not meaningfully enhance the parameter's meaning, so the baseline 3 for high schema coverage 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?

The opening sentence precisely states the resource ('one known public MaxVideoAI model') and what it returns ('exact current capabilities, limits, evidence and reviewed official promptingSources'). It also differentiates this tool from list/recommend siblings by requiring a known model, and explicitly rules out pricing and generation use cases.

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 gives a clear when-to-use condition ('when the user needs exact current capabilities...'), practical timing ('before budgeting or quoting'), and explicit exclusions ('Do not use this for exact pricing, generation, hidden models, or provider guarantees'). It does not name a specific sibling tool as the alternative, but the schema's mention of list_models and recommend_models provides some routing context.

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