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. The word 'List' implies a read-only operation, but it does not explicitly confirm non-mutating behavior or mention configuration requirements or error conditions, which is a minor shortfall.

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 that conveys the purpose and scope without any filler. Every word earns its place, and no redundant information is present.

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 tool, the description adequately covers the expected output (model IDs) and the endpoint context. However, it does not specify the exact return format or behavior in case of configuration or connectivity issues, leaving a slight 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 has zero parameters, so the input schema is empty. The baseline of 4 applies because there is nothing to add beyond the schema; the description correctly avoids mentioning parameters that don't exist.

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 uses a specific verb ('List') and clearly identifies the resource ('Gemini model ids on the configured OpenAI-compatible endpoint'). This unambiguously distinguishes it from the sibling describe_image, which deals with images.

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?

While there is no explicit when-to-use or alternative guidance, the context is clear: this tool should be used to enumerate available model IDs. Given the only sibling is describe_image, there is no ambiguity about when to select this tool.

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