list_models

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

With no annotations provided, the description carries the full burden for behavioral disclosure. It states the action (list all available models) and the return content (capabilities and languages), but it does not mention pagination, authentication, or any other behavioral traits. This is a minimal but sufficient disclosure for a simple read operation.

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 a single sentence that starts with the action ('List all available...') and delivers complete information without any waste. Every word contributes to the purpose.

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 there is no output schema, the description partially explains return values by mentioning 'capabilities and supported languages.' However, it does not detail response structure or potential edge cases, but for a simple list operation this is adequate.

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?

The tool has zero parameters, so the baseline is 4. The description doesn't need to add parameter context; the empty schema already signals no inputs are required.

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 uses the specific verb 'List' with the resource 'ElevenLabs TTS models' and specifies scope ('all available') and content ('capabilities and supported languages'). It clearly distinguishes from sibling tools like list_voices, which target voices.

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 usage when you need to enumerate all models, but it does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. Sibling tools like list_voices suggest a need for differentiation, but the description alone doesn't provide it.

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