compare_gpu_workload

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

With no annotations and no output schema, the description carries the full burden and does substantial work: it discloses that two cost metrics are returned (absolute hourly cost and $/GPU/h), that these can point at different clouds, and that over-provisioning is surfaced when only larger SKUs match. Remaining gaps are no-match/error behavior and pricing freshness, which an agent would want before relying on results.

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 core purpose and return semantics are front-loaded in the first two sentences, with supported values and edge cases following. It is dense but every sentence carries information; the parenthetical 'they're often different clouds' is the only mild filler.

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?

For a 3-parameter tool with no annotations and no output schema, the description covers purpose, supported inputs, dual return metrics, and the over-provisioning edge case. What's missing is failure behavior when no SKU matches and any statement about price data recency, which keeps it short of complete.

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 67%, and the description adds genuine meaning beyond it: it enumerates the accepted GPU families and clouds, and explains that gpu_count interacts with SKU availability (asking for 1 H100 may only match 8x bare-metal). The targets parameter's behavior is only restated from the schema, so it adds nothing there.

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 states a specific verb and resource — 'Cross-cloud GPU pricing comparison' that 'finds the cheapest matching SKU per cloud for a (gpu_type, gpu_count) request' — and scopes the supported GPU families and clouds. This distinguishes it clearly from siblings like compare_clouds, compare_workload, and the per-provider get_*_price tools without requiring the schema to be opened.

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?

It gives a clear usage context: 'Use for AI/ML workload pricing decisions — training, inference, fine-tuning,' which tells the agent the domain of applicability. However, it never names an alternative or exclusion — it doesn't say when to prefer compare_workload, compare_spot, or compare_total_cost_of_ownership instead, leaving sibling routing to inference.

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