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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, and the description adds meaningful behavioral context: the 0.05 USDC per-call cost, how aws-spot/gcp-spot rows are computed from spot price events, and data coverage start dates. This goes beyond what annotations alone provide, though it does not describe output format or pagination.

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 every sentence earns its place: cost, core output, per-day metrics, special spot-row methodology, and history availability. It front-loads the most important facts and avoids filler.

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 still explains the return shape—snapshot day, provider, market, quote count, and low/median/mean/high prices—and covers cost and data caveats. It lacks only minor operational details like default ordering or response size, but an agent has enough to call and interpret the tool correctly.

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 description coverage is 100%, so the schema already documents gpu, from, and to. The description clarifies that the result is a per-model index, which reinforces the role of the gpu parameter, but it does not add substantial parameter-level meaning beyond the schema.

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 defines the tool's output: a daily USD per GPU-hour index per provider and market for one GPU model, including quote counts and min/median/mean/max prices. It also distinguishes this from raw quote or event tools by describing the hyperscaler spot leg computation, so an agent can tell it apart from siblings like gpu_quotes or spot_daily.

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 implies the use case—historical daily indexed prices per provider and market—but it never explicitly states when to prefer this tool over siblings such as gpu_quotes, gpu_snapshot, or index_history. No when-not-to-use guidance or alternative routing is given.

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