list_models

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

No annotations are provided, so the description carries full burden. It discloses that the tool returns 'model list with capabilities' which gives some behavioral context about the output. However, it doesn't mention important behavioral traits like whether this is a read-only operation, if there are rate limits, authentication requirements, or what format the capabilities information takes.

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 perfectly concise and well-structured: two sentences that each serve distinct purposes. The first states what the tool does and what it returns. The second provides explicit usage guidance. There is zero wasted language or 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 no output schema, the description provides adequate but minimal information. It explains the purpose and when to use it, but doesn't describe the return format in detail ('model list with capabilities' is vague) or mention any prerequisites or constraints. For a simple list operation, this is acceptable but could be more complete.

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 0 parameters with 100% schema description coverage. The description doesn't need to explain any parameters, which is appropriate. The baseline for 0 parameters is 4, and the description correctly focuses on the tool's purpose and usage rather than parameter details.

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: 'Lists available TTS models' specifies the verb (list) and resource (TTS models). It distinguishes from siblings like list_agents or list_conversations by focusing on TTS models specifically. However, it doesn't explicitly differentiate from text_to_speech or text_to_speech_v3 tools which might also involve model selection.

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 when: choosing between v2, v3, or other models.' This gives clear context for when to invoke this tool. However, it doesn't specify when NOT to use it or mention alternatives like checking subscription status or fetching tags that might also inform model selection.

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