list_benchmark_models

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

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says the tool lists available models. It does not disclose output shape, pagination behavior, authorization requirements, or whether the list is user-scoped or global, leaving significant behavioral ambiguity.

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, direct sentence with no filler or redundant detail. It is appropriately concise for a no-parameter list operation and front-loads the essential action and object.

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-parameter list operation, the description covers the basic operation and object. However, with no output schema and no annotations, it does not explain what a 'benchmark model' is or how the returned list is scoped, leaving some ambiguity in the context of many sibling list tools.

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 an empty input schema, so there are no parameter semantics for the description to explain. The baseline of 4 for zero-parameter tools applies here because no parameter documentation 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 specific operation ('List') and a specific resource ('available benchmark models'), making the tool's purpose clear. However, it does not clarify how 'benchmark models' differ from regular models listed by the sibling tool list_models, so differentiation relies mostly on the tool's name rather than the description.

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 such as list_models or list_benchmark_tasks. There is no mention of context, exclusions, or preferred alternatives, so an agent receives no decision support beyond the tool's name.

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