List GPTProto models

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety and state change expectations. The description adds 'live' (models are currently available) and 'broad capability' (filter is coarse-grained), which are useful beyond the annotations. It does not describe output format or pagination, but the annotation coverage lowers the burden.

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?

Two sentences with no filler. The primary action and filter are front-loaded in the first sentence, and the usage guidance is in the second. Every word earns its place.

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?

The tool is simple: one optional parameter, no output schema, and annotations already establish safety. The description covers the action, scope, filtering, and a usage context. It does not explain what fields the returned list contains, but for a listing tool meant for pre-selection, the provided information is sufficient for most agents without additional detail.

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?

Schema description coverage is 0%, so the description must compensate. It explains that 'capability' is optional and is a 'broad capability', which maps directly to the single parameter. The enum values (text, image, video, audio) are present in the schema, so the agent can see the valid options. However, the description does not clarify the default behavior when the parameter is omitted, though 'optionally filtered' implies returning all models.

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 ('live GPTProto models'), plus an optional filter. This distinguishes it from siblings like model_describe (which focuses on a single model) and status/pricing. It does not explicitly name a sibling, but the list/describe contrast is evident.

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 phrase 'Use before choosing a model' provides a clear when-to-use context, implying this tool is for initial selection. However, it does not state when not to use it, nor does it name alternatives such as model_describe for deeper details on a selected model. Guidance is present but minimal.

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