list_models

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

With no annotations, the description carries the full burden. It discloses the return shape (OpenAI-compatible model objects with id, modality, context_window), the keyless pay-as-you-go nature, and the authentication requirement. However, it doesn't explicitly state that the operation is read-only or describe pagination/error behavior, so it's not fully transparent but still strong.

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 three sentences, each earning its place: purpose, return values and usage, and auth requirement. It is front-loaded with the main action, uses no filler, and is easily scannable.

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, no-output-schema tool, the description covers the necessary context: what the tool returns, how to use the returned data, and auth. It could optionally mention pagination or read-only semantics, but it is sufficiently complete for an agent to invoke and interpret results.

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?

There are zero parameters, and the schema coverage is 100% (empty object). The baseline for 0 params is 4. The description adds context about the returned id format and how to use it, which is relevant to parameter-like behavior but not required since no params 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 opens with a specific verb+resource: 'List the models available on Floe Inference'. It clearly identifies the tool's scope and distinguishes it from sibling tools by naming the specific gateway and the types of model objects returned (OpenAI-compatible with fields like id, modality, context_window).

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 explicit usage guidance: the returned id should be used with specific endpoints, and estimate_inference_cost is suggested as an alternative for pricing calls. It also states the authentication prerequisite (any Floe API key). This clearly helps the agent decide when and how to use the tool.

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