Snap: list available models

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

With no annotations, the description carries the behavioral burden and discloses that this is a GET to /v1/models scoped to the configured API key and returns an OpenAI-compatible model list. It does not mention error behavior, rate limits, or authentication failure modes, but it provides solid context for a read-only discovery tool.

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 two sentences and front-loads the primary purpose. The second sentence is slightly compressed but earns its place by connecting returned IDs to downstream model usage.

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?

There is no output schema, so the description must explain what comes back; it does so by saying the tool returns model IDs in an OpenAI-compatible shape. For a zero-parameter listing tool, this is nearly complete, with only minor gaps around error or empty-list behavior.

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 takes zero parameters, so the baseline is 4. The description does not explicitly say no inputs are required, but the empty schema makes that clear and there are no parameter semantics to compensate for.

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 gives a specific verb and resource: list model IDs available to the configured SubQ API key, with the backing endpoint (GET /v1/models) and response shape (OpenAI-compatible). It also differentiates this tool from the Decisions siblings by noting that those tools need a decision model such as subq-snap-1.1.

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?

It states the intended flow: call this tool to discover model IDs, then use a returned id with the endpoint that supports that model, and it calls out that the Decisions tools require a decision model such as subq-snap-1.1. It does not explicitly state when not to use it, but the usage context is clear.

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