apps__beta_tester_usages__metrics

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

Annotations already declare readOnly and idempotent hints, so the safety profile is covered. The description augments this by clarifying the response nature ('aggregated time-series data, not individual records'), which sets expectations about the output beyond what annotations provide.

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 concise sentences with the endpoint included. Every word earns its place, and the key scoping information is front-loaded.

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 metrics-read tool with 5 parameters and no output schema, the description provides enough context to know the purpose and output type. It could detail the time-series structure, but the endpoint reference and aggregated data note make it reasonably 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 100%, so the parameters are fully documented in the schema. The description does not add any detail about parameters, but the baseline of 3 is appropriate since the schema carries the weight.

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 a specific verb ('Read') and resource ('beta tester usages metrics for an app'), and adds that it returns aggregated time-series data, not individual records. This distinguishes it from sibling tools like builds__beta_tester_usages__metrics and beta_testers__beta_tester_usages__metrics by scoping to the app level.

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 gives clear context: use this for reading app-level beta tester usage metrics. It implies the distinction from build-level or tester-level metrics tools, but does not explicitly name alternatives or state when not to use this tool, so it misses the top score.

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