list_models

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns formatted model entries and indicates it is a read operation (no side effects implied). However, it does not explicitly state behavioral traits like idempotency or safety, but the simplicity of the tool makes this adequate.

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 two sentences, with the main purpose in the first sentence. Every sentence adds value: the first defines the output, the second explains the format and usage. No redundancy or unnecessary words.

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 simplicity (no parameters, no side effects) and that an output schema exists, the description is complete. It explains the return format and how to use the results with transcribe_audio. No additional information is needed.

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, and the schema coverage is 100% (empty schema). With no parameters, the baseline is 4. The description does not need to add parameter semantics beyond the schema, and it doesn't, but also doesn't detract.

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 that the tool returns installed transcription models. It explains the formatting of each entry (engine:model-id, display name, active status), fully clarifying its purpose. It distinguishes itself from sibling tools like transcribe_audio by providing the model ID format for direct use.

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 explicitly mentions that the returned engine:model-id can be passed as the model argument to transcribe_audio, giving clear usage context. However, it does not explicitly state when not to use this tool or mention alternatives beyond transcribe_audio, so it falls short of a 5.

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