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

Annotations already cover read-only/idempotent/closed-world, so the description's contribution is output semantics: China SXM prices are quoted per 8-GPU node, cloud rentals carry 7-day change and daily median history since 2026-10-18, and few_samples flags thin markets. That is genuinely useful context an agent cannot get from annotations. It still omits result size, ordering, and what an omitted query or boolean toggles do.

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?

It is a single sentence, but a large fraction is a parenthetical model-name enumeration (B300/B200/H200/... RTX 5090/4090D) that could be compressed. The purpose is buried mid-sentence rather than front-loaded, though the coverage list does help an agent check whether its specific GPU is present.

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, the description appropriately describes returned fields (VRAM, bandwidth, FP16, list price, China price, cloud rental with change and history). However, for a 3-parameter, zero-required lookup tool it leaves the boolean semantics, default behavior, and response shape underspecified.

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 only 33%: query is documented, but with_cloud_rental and china_available_only are bare booleans. The description indirectly explains these by mentioning cloud rental data and China market prices, but never states that the booleans gate those sections or what the defaults are, so it only partially compensates for the gap.

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 enumerates the resource (GPU models, VRAM, bandwidth, compute, list/China/cloud prices) but never states a verb — it is a field inventory rather than a statement of what the tool does. An agent can infer it returns GPU price and spec data, and the GPU focus implicitly distinguishes it from list_llm_prices, but nothing is asserted explicitly.

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?

No guidance on when to call this versus list_llm_prices, estimate_self_hosting, or cheapest_model_for_score, all of which are plausible alternatives. No prerequisites, no mention that all parameters are optional, no statement about what happens with an empty query.

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