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

No annotations are supplied, so the description carries the disclosure burden. It does well on data-coverage transparency (only RTX 4090 priced, others 未定) and lists the returned metrics, but it never states that the operation is read-only/no-side-effect, nor any auth or rate constraints — an important omission given zero annotation coverage.

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 tight sentences, front-loaded with the purpose and followed by the enumerated metrics and the coverage caveat. No filler; every clause carries information an agent needs.

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?

With no output schema and no annotations, the description must carry the return-value burden, and it does by naming each metric the response contains. The main remaining gap is behavior when the optional `gpu` param is omitted and the lack of a read-only guarantee, but for a simple two-param lookup this is close to sufficient.

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 50%: `gpu` has no schema description at all and `include_competitors` only says '默认 true'. The description partially compensates by implying `gpu` selects the GPU type and that 竞品中位价 is a returned field tied to include_competitors, but it never gives valid GPU-type values or explains what omitting `gpu` (0 required params) does.

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 (查询) and resource (GPU 租价), then enumerates exactly which price metrics are returned (即时档/排队档报价, host 实得, 电费成本, 竞品中位价, 国内零售价). This is distinguishable from siblings like model_requirements or fit_check, which deal with capability/fit rather than pricing.

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?

Usage is implied by the verb+resource — an agent can infer this is the tool for price lookups — and the scope caveat (只有 RTX 4090 有报价) usefully warns of sparse data. However, there is no explicit when-to-use/when-not guidance and no mention of the sibling tools (data_status, search_models) that might be the right first stop.

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