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

With no annotations, the description carries the full burden and does so exceptionally: it discloses data freshness (cached snapshot with as_of, not live), the payment requirement ($0.005 USDC on Base/eip155:8453), the exact failure mode (unpaid calls return HTTP 402 with PAYMENT-REQUIRED) and recovery (retry with PAYMENT-SIGNATURE), plus that tools/list is free. This is precisely the auth, cost, and staleness context an agent needs.

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 opening sentence is keyword-stuffing filler ('H100 price per hour, A100 price per hour, A100 rental price, Azure Spot GPU price') that the following sentence already covers more cleanly, so it does not earn its place and front-loads noise rather than purpose. The payment and freshness details are necessary and tightly written, but the redundancy costs it.

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 does a strong job covering cost, freshness, and provider coverage, and it hints at the return shape ('price lists with as_of'). It stops short of describing pagination/limit behavior or the default sort ordering, which the absent output schema would otherwise have to supply.

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 only 40% across 10 parameters, so the description should compensate more. It implicitly touches the sort dimension (hourly vs per-GPU-hour), the provider option, the offer_type option, and the query dimension (GPU models), but leaves region, min_gpus, include_fractional, limit, and the max_price filters unexplained. Partial but incomplete compensation.

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 and per-GPU-hour prices for data-centre GPUs) and scopes it precisely across GPU models (H100, H200, A100, L4...), providers (AWS, Azure, OCI), and offer types (on-demand, Azure-only Spot). An agent immediately knows this is a GPU price lookup, and the sole sibling (uk_vat_calc) is unrelated so no disambiguation is needed.

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 clearly frames when the data is appropriate by stating it is a 'Cached snapshot... not live' and noting the Spot availability limitation (Azure-only in the current snapshot), which tells the agent when results may be incomplete. It also spells out the invocation path (payment flow for tools/call). There are no competing alternatives to exclude, but no explicit exclusion language either.

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