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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explicitly states that results are ranked cheapest-first by price_per_gpu_hr and details filter semantics (e.g., country accepts ISO-2, comma lists, or 'EU'). It does not disclose potential side effects (likely none for a search), authentication requirements, or rate limits, but the core behavior is transparently described.

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?

The description is a compact set of sentences that front-loads the primary action and immediately explains key filter semantics. Every sentence earns its place—there is no fluff or repetition. The structure is easy to scan and digest.

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?

Given the complexity (10 optional parameters, no annotations) but the presence of an output schema, the description covers the main filter behaviors and the result ordering. It does not mention pagination or error handling, but the limit parameter is present in the schema and the return format is presumably covered by the output schema. Overall, it provides sufficient detail for correct invocation.

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 0%, so the description must compensate. It adds meaning to five parameters (gpu, offer_class, country, min_gpu_count, region) with concrete examples and constraints, but leaves others (limit, provider, min_vram_gb, min_reliability, max_price_per_gpu_hr) to their self-explanatory names. It adds significant value for the most nuanced parameters but does not fully cover all ten.

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?

The description opens with a specific verb-resource pair, 'Search live GPU offers,' and clarifies the domain with concrete GPU model examples ('4090', 'H100'). It differentiates from siblings by focusing on the search/listing behavior rather than retrieval or rental actions, making its purpose instantly clear.

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?

While the description implicitly positions this as the tool for exploring available offers, it provides no explicit guidance on when to choose it over alternatives like get_offer or best_value. It lacks direct statements such as 'use this to browse offers before renting' or 'for a specific offer, use get_offer'. The usage context is implied but not spelled out.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.