list_models_tool

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

No annotations are provided, so the description carries the full burden. It only says what is listed; it says nothing about auth requirements, rate limits, or that include_unverified defaults to false and exposes suspect endpoints. For a read-only listing tool this is low risk, but the disclosure is still thin.

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 efficient, front-loaded sentence with no waste. It is arguably too terse for the tool's filtering capability, but nothing is padded.

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?

An output schema exists, so return values need not be explained, and the schema covers all three parameters. The description is still minimal for an agent deciding between this and recommend_model_tool, leaving a routing 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?

Schema description coverage is 100%, so the schema already documents kind, backend, and include_unverified fully. The description adds nothing about filter semantics, so the baseline of 3 applies.

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?

States a clear verb (List) and resource (Higgsfield models) with scope ('every supported'). It does not distinguish itself from the sibling recommend_model_tool, which also concerns model selection, and 'every' sits awkwardly against the kind/backend filters in the schema.

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 versus alternatives like recommend_model_tool or validate_params_tool, and no note that the filters exist as a way to narrow results. The agent must infer usage entirely.

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