list_models

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

With no annotations provided, the description carries the burden of behavioral disclosure. It adds useful context: it's a read-only listing (implied by 'List'), scope is 'available on the account', and it enumerates the returned fields (model_id, name, can_do_tts, etc.). This exceeds the bare verb and gives the agent a good sense of what to expect.

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 extremely concise: two short sentences plus a field listing. Every sentence earns its place, and the most important information (what it does) is front-loaded. The field list is presented as a compact inline list, avoiding redundancy.

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 no parameters and an output schema exists, the description is complete. It tells the agent what the tool returns, the scope (account), and the practical use case. There are no gaps that would leave the agent confused about when or how to invoke this tool.

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 description doesn't need to explain parameter semantics. The schema is empty, and the baseline for 0 params is 4. The description does mention how to use the returned model_id (via `model=`), which is a nice touch beyond 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's purpose: 'List ElevenLabs models available on the account, with capability flags.' It uses a specific verb (List), identifies the resource (ElevenLabs models), and specifies the account scope. It also distinguishes itself from sibling listing tools by focusing on models and their capabilities.

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 usage guidance: 'Use to pick or confirm a model id before pinning it via `model=`.' This tells the agent when to use the tool. It doesn't explicitly exclude alternatives, but the sibling tools are clearly for other resources (voices, profiles, etc.), so the context is clear.

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