get_usage_events

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

With no annotations, the description carries full burden. It describes the returned data (model, token counts, costs, chargeable) and filtering, but does not explicitly declare read-only nature, pagination behavior, or any side effects. The word 'Get' implies read but not explicitly.

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?

Two focused sentences: first defines the resource and data fields, second adds filtering options. No redundant words.

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 parameter count (5) and schema descriptions, the description covers core purpose and filtering. It could mention pagination or ordering but schema provides page/pageSize defaults. Output format is not described but no output schema exists.

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 100%; each parameter has a description. The description adds context for email and date filtering but does not significantly enhance understanding of page or pageSize beyond schema defaults.

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 retrieves granular per-request usage events with specific data fields (model, token counts, costs, chargeability). This distinguishes it from sibling tools like get_daily_usage or get_model_usage that aggregate data differently.

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 mentions filtering by user email and date range, implying use cases. However, it does not explicitly state when to use this tool over siblings (e.g., daily vs per-request) or when not to use it.

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