Per-model cost & token breakdown

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 the temporal scope (billing cycle), the grouping (by model), the output metrics, and a key behavioral trait: it is heavier than get_usage. However, it does not explicitly state read-only/non-mutating behavior, though 'shows' implies it.

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?

Three sentences, each earning its place: output description, usage trigger, and performance caveat. Front-loaded with the core purpose, no redundancy.

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 zero-parameter tool with no output schema, the description is remarkably complete: it covers what it returns, when to use it, and how it compares to the sibling tool. No essential context is missing.

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?

The tool has zero parameters, so the baseline is 4. The description adds context about the output breakdown but no parameter details are needed since there are none.

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 clearly states the tool shows this billing cycle's usage broken down by model, including specific metrics (cost, request count, token totals). The verb 'shows' and the resource 'usage breakdown by model' are specific and the tool is distinguished from get_usage by the granularity of the breakdown.

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?

Explicit when-to-use guidance is provided: 'Use when the user asks what's costing them or which models they use most.' It also names an alternative (get_usage) and gives a performance-based exclusion: 'Heavier than get_usage, so call it on request rather than every task.'

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