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GPU types tracked for rental prices

list_gpus
Read-onlyIdempotent

GPU types whose rental price InferIndex tracks (H100, A100, L40S…), with the lowest price per GPU per hour in USD for each tier (guaranteed, community, spot) and the number of GPUs of that configuration. Prices are per GPU per hour, as published by each provider, and dated. Tiers are different products and are never compared with each other.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description adds real semantic context beyond that: prices are per GPU per hour as published by each provider and dated, and the explicit rule that tiers are different products and are never compared with each other — a caveat that prevents incorrect cross-tier aggregation.

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, front-loaded with what is tracked and then the per-tier pricing rule. Dense but every clause carries meaning; the parenthetical examples (H100, A100, L40S) and tier list are useful rather than padding.

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?

With no output schema, the description must describe the return payload, and it does so precisely: one row per tracked GPU type with lowest price per GPU/hour, tier, and GPU count. It omits ordering and any notion of how many entries come back, but for a small enumerated list this is close to complete.

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?

The tool takes no parameters, so the schema cannot carry semantic load and the baseline for a zero-parameter tool applies. The description appropriately spends its words on what is returned rather than on nonexistent inputs.

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?

States a specific resource (GPU types tracked by InferIndex) and enumerates the returned fields: lowest price per GPU/hour in USD per tier, tier names, and GPU counts. This is far more specific than a tautology, though it never names a sibling tool to distinguish itself from 'cheapest' or 'gpu_rentals'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance and no alternatives named, despite siblings like 'cheapest', 'gpu_rentals', and 'compare_providers' that plausibly overlap. The only implicit cue is that this lists tracked GPU types rather than answering a pricing question, which the agent must infer.

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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