list_models

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

With no annotations, the description must carry the full behavioral burden. "List" implies a read-only, non-destructive operation, and the model characterizations add useful context, but it discloses nothing about auth requirements, rate limits, or whether results change over time.

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?

Two tight sentences: the first front-loads what is returned, the second packs concrete, decision-relevant trade-offs with no filler. Every clause 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?

For a zero-parameter list tool with no output schema, the description adequately explains what comes back (models with languages) and why one model differs from another. Return-field details are the only minor omission, which is acceptable given the tool's simplicity.

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 takes zero parameters, so the baseline is 4. There are no arguments whose meaning could be clarified or omitted.

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?

States the resource plainly (speech models and their languages), which cleanly separates it from the sibling list_voices (voices) and list_outputs (generated output). It stops short of an explicit verb like "list all available," but the intent is unambiguous.

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 offers genuine model-selection guidance (eleven_v3 for expressiveness, eleven_multilingual_v2 for stable long narration, flash/turbo for speed and cost), which helps an agent pick a model. However, it gives no explicit guidance on when to call this tool versus alternatives like list_voices, leaving tool usage to inference.

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