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

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds genuinely new traits: results are sorted by popularity, `can_use=false` signals plan ineligibility, and the token must be passed through unchanged. It stops short of describing pagination or result-count behavior against `limit`.

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?

Three tight sentences, front-loaded with what the tool does and immediately followed by the actionable token contract. The closing preference hint is short and non-redundant; no sentence is filler.

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 returns and does so for the fields that matter most (the `voice` token and `can_use`). Other returned attributes such as name, style, gender or persona are left unmentioned, a minor gap for a discovery tool.

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 67%, so roughly a third of parameters rely on the description. The description clarifies the `can_use`/`only_usable` relationship but says nothing about `limit`, `gender`, or how `query` interacts with `language`, so it only partially compensates for the coverage gap.

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 with scope: 'Search the AI voice catalog for one language.' It also makes the downstream purpose concrete by naming the tools the returned `voice` token feeds into (text_to_speech, multi_voice_to_speech, srt_to_speech), which lets an agent place it precisely against siblings like get_voice_details, list_favorite_voices and list_my_custom_voices.

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 clear context: use it to obtain a `voice` token for TTS calls, and prefer the user's favorite or custom voices when they exist. It does not explicitly name list_favorite_voices or list_my_custom_voices as the alternatives for that preference, so the routing is implied rather than spelled out.

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