metering__list_events

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

No annotations are provided, so the description carries the burden. It discloses read-only nature, pagination via next_cursor, and test-event exclusion. However, it lacks details on rate limits, authentication, or error 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 description is a single sentence with front-loaded key information ('Read usage events (paginated, read-only)') followed by a list of filters. It is concise and clear, though slightly dense.

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 10 parameters, no output schema, and no annotations, the description covers the main purpose and filters but lacks details on return format, pagination mechanics, and parameter constraints. It is adequate but not fully 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%, so the description must compensate. It lists filters and hints at enum values (e.g., consumer_class: internal|external|unknown; outcome: ok|error), but does not provide full value sets or format details for parameters like since/until.

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 it reads usage events, is paginated, and read-only. It lists filters, making the purpose specific. However, it does not explicitly differentiate from sibling tools like metering__usage_summary or metering__usage_timeseries.

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 enumerates filters and mentions test-flagged event exclusion, providing usage context. It does not give explicit when-to-use or alternatives, leaving some ambiguity.

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