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rent_best

One-shot rent: search the live book with these filters and place on the best (cheapest) matching offer — 'cheapest H100 under $2/hr, go'. Same guarantees as rent_offer (dry-run default, live re-quote, your max_price_per_gpu_hr is absolute). If the best offer is already gone it walks to the next, at most 3, and reports what it skipped.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpuNo
imageNo
labelNo
api_keyYes
confirmNo
countryNo
disk_gbNo
dry_runNo
providerNo
min_vram_gbNo
offer_classNo
account_tokenNo
min_gpu_countNo
idempotency_keyNo
max_price_per_gpu_hrYes
auto_destroy_budget_usdNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.9/5.0
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. It discloses dry-run default, live re-quote, absolute price cap, bounded retry behavior (at most 3), and reporting of skipped offers. This is meaningful behavioral context beyond a simple 'rent cheapest' statement.

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?

The description is compact and front-loaded with the core action, then adds essential behavioral guarantees and fallback logic. No sentence is wasted, though the dense parenthetical list and example could be slightly clearer.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 16-parameter tool with no annotations and no output schema, the description covers the key behavioral contract (dry-run, re-quote, price cap, walk-to-next) but leaves many filter parameters unexplained and does not describe the response shape beyond 'reports what it skipped'. It is adequate but incomplete for full autonomous invocation.

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

Parameters2/5

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

Schema description coverage is 0%, and the description only explains max_price_per_gpu_hr (absolute) and dry_run (default true). The other 14 parameters, including gpu, image, provider, min_vram_gb, confirm, and idempotency_key, are not given any semantic explanation beyond their raw titles, so the description does not compensate for the schema gap.

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?

States a specific verb and resource: it searches the live book and rents the best (cheapest) matching offer. It distinguishes itself from siblings like rent_offer and search_offers by framing itself as a one-shot best-offer action, so an agent can tell what it does.

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?

Explicitly conveys when to use it via the example intent ('cheapest H100 under $2/hr') and references rent_offer's contract for guarantees. It does not name alternative tools for exclusion, but the 'one-shot' phrasing and 'walks to the next' behavior imply a fast auto-select path versus manually searching then renting.

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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