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. It discloses that the tool lists models with performance characteristics, which implies read-only behavior, and mentions environment variable configuration context. However, it doesn't detail behavioral traits like whether it requires authentication, rate limits, error conditions, or the format of returned data. It adds some context but lacks comprehensive behavioral disclosure.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by two sentences providing useful context. There's minimal waste, though the second sentence about environment variable configuration could be more tightly integrated. Overall, it's efficient and well-structured.

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's low complexity (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It explains what the tool does and provides context about model configuration. With an output schema present, it doesn't need to detail return values. However, it could better address usage scenarios or integration with sibling tools to be fully 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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's purpose and context. This meets the baseline of 4 for zero-parameter tools, as it adds value without redundant information.

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 available Whisper model sizes with performance characteristics.' It specifies the verb ('List') and resource ('Whisper model sizes'), but doesn't explicitly differentiate from sibling tools like 'check_backends' or 'transcribe_audio'. The purpose is specific but lacks sibling comparison.

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 no guidance on when to use this tool versus alternatives. It mentions configuring the active model via an environment variable and the default, but doesn't explain when an agent should call list_models (e.g., before transcription to choose a model, for system setup, etc.) or how it relates to sibling tools like transcribe_audio. No explicit when/when-not or alternatives are provided.

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