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

The description adds meaningful behavioral context beyond the readOnly/openWorld/idempotent annotations: the 0.05 USDC per call cost, the median-across-regions calculation, division by GPUs per node, inclusion of lo/hi and discount, and exclusion of neo-cloud commitment prices. It does not contradict the annotations.

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 dense but economical, front-loading the paid-call warning before the technical definition and the illustrative question. Each sentence carries useful information, though the middle sentence is somewhat heavy with punctuation and nested clauses.

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?

For a single-parameter tool with no output schema, the description covers input semantics, output contents, calculation methodology, cost, and exclusions. It is sufficient for an agent to decide when to call it, though the exact response shape is not described.

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 schema already documents the single `gpu` parameter with an example and description at 100% coverage. The description reinforces that the tool handles one GPU model, but adds little semantic detail beyond what the schema provides, so the baseline of 3 is appropriate.

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 clearly states the tool returns term structure (spot, on-demand, 1y, 3y) per hyperscaler for a single GPU model in USD per GPU-hour, with median, lo/hi, and discount details. It is specific about verb and resource, but it does not explicitly differentiate itself from the sibling tool term_instance, which likely covers a different granularity.

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 gives clear usage context via the quoted example question 'what does a committed H100 cost against spot and on-demand, per cloud' and states exclusions such as neo-cloud quote-only prices. However, it does not explicitly say when to use this tool instead of related siblings like reserved, ondemand, or term_instance.

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