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

Beyond the annotations (readOnly=false, idempotent=false, destructive=false), the description discloses two important traits: the call consumes characters from the user's balance (minimum 50) and the generated voice is not saved. It does not cover latency, failure modes, or rate limits, so it stops short of full behavioral disclosure.

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-loading the core action, then the cost constraint, then the persistence caveat. No wasted words and every sentence earns its place.

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?

Covers what an agent needs to call it correctly: cost, non-persistence, and the sampleUrl handle for the follow-up save_voice call. With no output schema present, the return shape beyond sampleUrl is left implicit, which is the only notable gap.

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%, so the baseline is 3. The description adds a useful hint that the 'description' parameter should encode age, gender, accent, tone and energy, which goes slightly beyond the schema's single example, but it adds nothing about 'language' or the 'text' constraints.

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: create a brand-new AI voice from a description and hear a sample line. It also distinguishes itself from the sibling save_voice by explaining that this tool does not persist the voice, so an agent can tell the two apart without opening either schema.

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 use cases (character, brand, narrator) and an explicit follow-up path: call save_voice with the returned sampleUrl to keep the voice. It does not contrast with generate_speech or search_voices, so alternatives are only partially mapped.

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