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GrabGPU

find_gpu

Find cloud GPUs to rent now: price per GPU hour and the stock each provider reported at the last daily check. Rentable offers come first, cheapest first. Give the buyer rent_url; it opens the provider (some are referral links, same price for the buyer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuNoGPU model, e.g. "H100", "B300", "RTX 4090". Omit for every model.
limitNoHow many offers, default 20, at most 100.
in_stockNoOnly offers the provider reports in stock. Default true.
min_gpusNoSmallest node size needed, e.g. 8 for an 8x node.
max_priceNoHighest USD per GPU hour.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does disclose real behavioral traits: data freshness is limited to the last daily provider check, results are ordered rentable-first then cheapest-first, and some rent_urls are referral links that do not change the buyer's price. It omits rate limits, error behavior, and pagination beyond the limit parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the core action and return content, followed by ordering rules and the referral disclosure. No filler or repetition of the schema.

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 adequately characterizes the return data (price per GPU hour, reported stock, rent_url) and its ordering, which is what an agent needs to interpret results. Minor gaps remain around how stale/null pricing or out-of-stock entries appear in results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all five parameters (gpu, limit, in_stock, min_gpus, max_price) are already fully documented with examples and defaults. The description adds no parameter-level detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ("Find cloud GPUs to rent now") and immediately defines what the result contains: price per GPU hour and reported stock. It is unambiguous what the tool does, and no sibling tools exist to be confused with.

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

Usage Guidelines4/5

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

It gives clear context for use (finding GPUs available to rent now, with rentable offers surfaced first and sorted cheapest-first) and tells the agent what to do with the result ("Give the buyer rent_url"). There are no alternatives to exclude, so no when-not guidance is possible, but explicit selection criteria are absent.

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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