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

Annotations only state that the operation is read-only/open-world, so the description carries the behavioral burden. It candidly discloses that explanation invokes a language model, costs roughly 30x more, and can be skipped, and details exactly what the result contains.

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 dense with no filler: purpose first, then return shape, then the cost tradeoff, then the error entry structure. Every sentence contributes behavior an agent would otherwise not know.

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-required-param tool with an output schema, the description is nearly complete and even enriches the output shape. The main gap is undocumented language behavior and audio encoding, which could matter for a pronunciation scorer.

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 0%, so the description must compensate; it maps audio to the recording, text to the intended sentence, and explain to the cheaper no-explanation mode. However, language is left entirely to the schema's bare default, and there is no guidance on accepted audio format/value type.

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 first sentence names a specific verb ('Compare') and resource ('a recording against the sentence it was meant to say, phoneme by phoneme'), making the tool's job unmistakable. This also distinguishes it from transcription siblings like transcribe_audio, which have no known reference text.

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 implies the tool is for pronunciation comparison against known expected text, but never explicitly contrasts it with alternatives in the sibling set. It does provide valuable in-tool guidance: set explain=False when you want to avoid the expensive LLM explanation.

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