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 of behavioral disclosure. It implies a read-only enumeration but does not state whether results are paginated, complete, cached, or scoped to an account/workspace. That is a meaningful gap for a tool whose only behavior is its return.

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?

A single, front-loaded sentence with zero waste. It is appropriately sized for a zero-argument tool, though it is terse to the point of leaving the resource underspecified.

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?

For a zero-param, no-output-schema tool the description is only minimally sufficient. It omits what 'models' refers to and what the returned list contains, leaving the agent to guess the domain. Adequate but with a clear gap.

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 takes zero parameters, so there is nothing for the description to explain and the baseline of 4 applies. The description adds no parameter information because none is needed.

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 ('List') and resource ('models'), so the basic operation is unambiguous. However, 'models' is undefined in context — the sibling set is dominated by task/step/agent/key tools, and it is unclear whether these are LLM models, agent models, or something else. No sibling differentiation is provided because none of the listed siblings obviously overlap, but the resource itself remains ambiguous.

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?

There is no guidance on when to call this tool or what it should be used for. With no parameters and no stated context, an agent has to infer the purpose entirely. No alternatives or preconditions are mentioned.

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