list_audio_models

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It reveals the return contents in detail and notes that 'Every model works with every voice,' which is a non-obvious behavioral trait. It also labels itself as the 'authoritative source' for model acceptance, setting expectations for reliability. It doesn't explicitly state read-only or error behaviors, but for a list operation, the transparency is strong.

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 and front-loaded with the main action and output details. The first sentence is packed with information but remains specific; the second clarifies the relationship with voices; the third gives usage guidance and parameter behavior. No filler words, though the first sentence is dense. It 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?

Given the tool has one optional parameter and no output schema, the description thoroughly covers what is returned (strengths, price, character limit, language coverage, audio types, output formats for audio_generate) and when to use it. It also addresses the relationship with voices and the authoritative nature of the data. This is complete enough for an agent to select and invoke the tool correctly.

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%: the schema already describes 'query' as 'Optional keyword to filter models by use-case (matches the name, display name, and strengths).' The description adds illustrative examples ('expressive', 'long-form', 'fast') and reinforces that filtering is by use-case keyword. This added value is helpful but not substantial beyond the schema, so the baseline of 3 is appropriate.

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 lists available speech models and details what attributes are included (strengths, price, character limit, language coverage, audio types). It distinguishes itself from list_voices by explicitly saying voices are a separate concern, and the resource is specific to audio models, setting it apart from sibling list_video_models and list_image_models.

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 provides explicit guidance on when to use this tool: 'call it when choosing a model for an open-ended request, or to check a value before setting it.' It also names an alternative: 'use list_voices for those' for voice-related queries. This gives clear decision rules for the agent.

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