list_cost_recommendations

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

Beyond the annotations, the description reveals important behavioral traits: it fans out across locations, skips empty or disabled recommenders, and auto-discovers locations via Asset Inventory when locations are omitted, including a dependency on the Cloud Asset API. These details materially help an agent predict execution behavior.

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 summary sentence fronts the core purpose, followed by a short behavior note and a compact Args section. Every sentence carries distinct information, and there is no filler or repetition of schema defaults.

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?

The description covers the main operational concerns: what is aggregated, how locations are handled, the Cloud Asset API requirement, the per-call limit, and environment selection. It does not describe the shape or pagination of the returned recommendations, but given the read-only and idempotent annotations plus the strong parameter detail, it is nearly 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 description coverage is 0%, but the description fully compensates by explaining every parameter: locations as comma-separated zones/regions with auto-discovery behavior, limit_per_call as a per recommender+location cap, and environment as a configured GCP environment selector with a default behavior. This is exactly the semantic enrichment the schema lacks.

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 and resource: 'Aggregate GCP cost-optimization recommendations (idle/rightsizing/etc).' This clearly distinguishes the tool from the sibling list_recommendations by narrowing scope to cost-optimization, and the behavior is further clarified with the fan-out and skip semantics.

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?

The description provides useful context about locations, auto-discovery, and environment selection, but it never explicitly states when to choose this tool over alternatives such as list_recommendations or get_cost_breakdown. Usage is implied by the purpose rather than explicitly contrasted with sibling tools.

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