列出可用的模型與思考深度

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

With no annotations, the description carries the full burden and does substantial work: it discloses the data is 'scraped live from its model menu,' that account tier differences are handled automatically, that `thinking` is empty for services without such a setting, and that it 'works as a guest.' It also documents the return shape (label/current). It stops short of explicitly stating side-effect-free/read-only behavior, so not a 5.

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?

Front-loaded with the core purpose, then return-shape details. Dense but each clause carries information (live scraping, guest access, thinking semantics). Parentheticals are slightly heavy but not wasteful.

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?

No output schema exists, and the description compensates by explaining the returned fields (`models[].label`, `current`, `thinking[].current`) and their edge cases. Combined with the guest/tier notes, an agent has enough to call and interpret results correctly; only explicit read-only framing is missing.

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 coverage is 100% and the single `provider` enum is fully documented in the schema, so baseline is 3. The description adds only the phrase 'chosen service' and does not enrich parameter meaning beyond the schema's enum descriptions.

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 specific verb and resource: 'List the models and thinking-depth options available for the chosen service.' It is immediately distinguishable from siblings like webchat_ask or webchat_status, and even names webchat_ask as the consumer of the returned labels.

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 explains the intended workflow — use `models[].label` with webchat_ask's `model` — which implicitly tells the agent when to call this (before asking, to discover valid model identifiers). It does not give explicit when-not conditions or name alternatives for listing, but the context is clear.

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