Genexxis paid agent tools (x402)

cloudprice_gpu

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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "cloudprice_gpu"
  }
}

Input Schema

Name	Required	Description
`type`	No	on-demand \| spot \| any (default any)
`limit`	No	1-50, default 15
`model`	Yes	GPU model, e.g. H100, A100, L40S

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A3.8/5.0

Behavior4/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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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