list_models

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

With no annotations, the description carries the full burden, and it does well: it discloses the live pricing behavior tied to the current BSV/USD rate, states the auth requirement explicitly ('No key needed'), and signals the proper call order. It does not describe the exact response shape, but for a simple catalog tool this is a minor gap.

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 short, information-dense sentences. The main action is front-loaded, followed by the key pricing detail and a practical usage instruction. No filler or repetition.

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 with no output schema and no annotations, the description provides the essentials: what is listed, in what unit, with what freshness, auth requirements, and when to call it. An exact return format would be beneficial, but the description is otherwise complete enough to invoke correctly.

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 parameter ambiguity is not a concern. The description additionally notes that no API key is needed, which covers the only setup detail an agent might otherwise wonder about.

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 and resource: 'List the models this BSV inference gateway sells.' It clearly identifies what the tool returns and adds a distinguishing detail (live retail price in satoshis per 1,000 tokens) that sets it apart from the sibling tools.

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 instruction 'Call first to choose a model' gives clear sequential context, and 'No key needed' removes a likely prerequisite concern. It does not explicitly name alternatives such as infer, but the intended role as a preliminary discovery step is clear enough.

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