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

The annotations already establish read-only, idempotent, non-destructive, closed-world behavior. The description adds non-schema context: the call consumes no reads and the results include read costs, video image-start/sound support, and model strengths, though it does not discuss auth or rate limits.

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 content is dense and mostly front-loaded, with little wasted wording, but the first sentence is awkward and run-on ('with about how many reads one file uses'), making the capabilities harder to parse than 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 two-parameter listing tool with no output schema, it discloses what the result set contains: model categories, read cost, video capabilities, and what each model is good for. It does not specify output shape or default behavior, but it gives enough context to invoke the tool correctly.

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?

With 0% schema description coverage, the description carries the parameter burden. It supplies the key kind values (image, voice, video) and explains photo=true as filtering to image-start/edit models; it does not restate defaults, but it adds the missing semantic mapping.

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 identifies the resource as models for generating pictures, voice-overs, music, or videos and says it returns read costs and capabilities. It is clear what the tool lists, though it does not explicitly distinguish itself from sibling tools such as make_media.

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?

It gives useful filter guidance: kind=image, voice, or video, and photo=true filters to models that start from or edit a picture. It also notes 'Uses no reads,' implying discovery before generation, but it does not explicitly say when to use this versus make_media or another sibling.

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