predict_duration

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

With no annotations, the description carries the full burden. It discloses key behaviors: no audio output, auto-chunking for long text, credit and latency scaling with text length, and that it predicts duration. It does not mention idempotence or authorization, but the disclosed traits are comprehensive for a prediction tool.

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 description is three sentences, each serving a distinct purpose: defining the action, linking to text_to_speech parameters, and providing scaling/credit context. No unnecessary words, highly efficient.

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?

The description is largely complete: it explains purpose, usage, key behaviors, and scaling. It does not cover error handling or prerequisites (like authentication), but for a prediction tool with an output schema, it is adequately comprehensive.

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 0%, but the description states 'Accepts the same parameters as text_to_speech', which adds meaning by linking to a known sibling. However, it does not detail individual parameters further, so it adds moderate value beyond the schema.

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 clearly states the tool predicts audio duration without producing audio, using verb 'predict' and resource 'expected output audio duration'. It distinguishes from the sibling 'text_to_speech' by emphasizing no audio file is produced.

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 explicitly tells the agent to use this to estimate credit cost before synthesizing, providing clear context. It also notes that it accepts the same parameters as text_to_speech, implying a cost estimation use case. However, it does not explicitly state when not to use it or mention alternatives, but the context is sufficient.

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