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

Beyond the readOnly/openWorld annotations, the description discloses the data source ('Official provider price APIs, refreshed daily'), the cost model ($0.005 USDC per request, one paid HTTP GET = one charge), the payment rails (x402 on Base, MPP on Tempo), and that 4xx errors are not charged. Those billing and freshness details are exactly the behavioral context an agent needs before invoking a paid 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?

Coverage, ranking unit, and data provenance are front-loaded in the first sentence, with filters and pricing terms following. The final sentence is dense with semicolon-separated clauses but each carries distinct billing information, so little is wasted.

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 must convey the return shape on its own; it specifies ranked USD-per-GPU-hour list prices across named providers and regions, which is enough to set expectations. It stops short of listing the full result fields or pagination behavior, which is a minor gap for a paid, all-optional-filter tool.

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%, so every parameter is already documented in the input schema, including the limit default of 20 and pricing values. The description reinforces the filter set and the USD-per-GPU-hour unit but adds no syntax or format guidance beyond what the schema already provides, so the baseline of 3 applies.

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 names a specific resource (hourly list prices for GPU/AI accelerator instances), enumerates the covered models (H100, H200, A100, B200, MI300X, Trainium, etc.), the clouds and regions, and the ranking unit (USD per GPU-hour). This is clearly distinguishable from the sibling ai_llm_prices, which covers a different resource class.

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 filter dimensions ('Filter by gpu model, cloud, region, pricing') and the pricing modes available (on-demand and Azure Spot), so an agent knows how to narrow results. It does not explicitly say when to prefer this over ai_llm_prices or what happens when no filter is supplied, 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.