Cheapest cloud GPU-hour for a GPU model (H100, H200, B200, A100, MI300X, L40S, L4, A10G, A10, V100, T4) across AWS and Azure regions, on-demand or spot, plus per-provider summary and 30-day price history. Price: $0.01 USDC per call, paid with x402 on Base.
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | on-demand | spot | any (default any) | |
| limit | No | 1-50, default 15 | |
| model | Yes | GPU model, e.g. H100, A100, L40S |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose the most consequential trait: a paid call at $0.01 USDC via x402 on Base. It also tells the agent what the response contains (cheapest price, per-provider summary, 30-day history). Gaps remain around wallet/auth requirements for x402 settlement and failure behavior when no price is found.
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?
Two sentences, zero filler, with the capability statement and coverage scope front-loaded before the price disclosure. The first sentence is a long run-on carrying many facts, but every clause earns its place.
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?
No output schema exists, and the description compensates by stating what comes back: the cheapest GPU-hour plus a per-provider summary and 30-day history. Combined with full schema coverage, an agent has enough to invoke correctly; only the payment/auth mechanics and no-result behavior are left unstated.
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 a 3 is the baseline, but the description adds real value by fully enumerating the valid `model` values (H100, H200, B200, A100, MI300X, L40S, L4, A10G, A10, V100, T4) rather than the schema's three examples. It also reinforces the on-demand/spot dimension of `type`. The `limit` parameter is not mentioned.
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?
Specific verb+resource ("cheapest cloud GPU-hour for a GPU model") with an explicit enumeration of supported models, providers (AWS/Azure), and pricing modes (on-demand/spot). It doesn't explicitly contrast itself with sibling tools cloudprice_compute and cloudprice_instance, which is the one thing an agent choosing between the three would want.
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?
Usage is implied by the model list and the on-demand/spot framing, but there is no explicit statement of when to pick this versus cloudprice_compute or cloudprice_instance. No prerequisites, no exclusions, no guidance on when the 30-day history or per-provider summary is the reason to call it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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