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 implies a read-only action (listing) but does not explicitly state side-effect freedom, output format, or pagination. For a zero-parameter list tool, the disclosure is minimal but not misleading.

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 a single, front-loaded sentence with an example. Every word earns its place, and it is immediately clear what the tool does. No redundancy or filler.

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, no output schema, and a simple purpose, the description is nearly complete. It tells the user what will be returned (the list of models). It could mention whether the list includes deprecated or experimental models, but such detail is not critical for correct invocation.

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 has 100% coverage (with no properties). The description adds value by explaining that the output is the set of models usable with --model, which is beyond the empty schema. Per the baseline for 0 parameters, a score of 4 is appropriate.

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 states a clear verb ('Lista') and a specific resource ('modelos disponibles para --model'), and includes concrete examples (gpt-5, sonnet-4-thinking). It unambiguously conveys the function and is distinct from sibling tools, which are unrelated (worktree, run, etc.).

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 specifies the intended use: to see which models can be passed to the --model option. This gives clear context for when to invoke the tool. It does not explicitly mention when not to use it, but there is no obvious alternative among siblings, so the guidance is sufficient.

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