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

Annotations already mark the tool read-only and non-destructive, and the description adds substantial context beyond that: predictions target deliberately hard benchmark conditions, the model is trained on thousands of real jobs, and all non-English languages score identically. This prevents misinterpretation of scores and clarifies how the languages parameter behaves. No contradiction with annotations.

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?

Four dense sentences, all containing valuable information, with the critical limitation and free-check framing placed upfront. The heavy use of caps and em-dashes is a little noisy, and the NOTE/SCALE sections could be more clearly separated, but there is no fluff.

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?

No output schema is provided, but the description explains what is predicted (recall and false-activation risk), the score scale, and the thresholds for action. The only minor gap is ambiguity about whether recall and false-activation risk are separate scores or a combined one, and there is no explicit statement of output format.

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 100%, so baseline is 3. The description adds meaningful semantics by explaining that the languages parameter is interpreted by English share only and that 2-4 syllable phrases work best. This helps the agent construct valid inputs, though the core parameter formats are already in 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 recall and false-activation risk for a wake word and functions as a free word-quality check. The verb 'predicts/check' and resource 'wake word' are explicit, and the 'before paying' framing distinguishes it from paid sibling tools like create_training_job.

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?

Strong 'when' guidance is present: 'use BEFORE paying' and 'do NOT reject words for scoring below ~80' plus 'only warn your human when the score is under ~50'. It also gives a key limitation (cannot rank non-English languages) that signals when not to rely on it. However, it does not explicitly name an alternative tool for language ranking or live testing.

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