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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description does not contradict them. It adds useful behavioral context by naming the exact metrics returned and the optional goal-revenue behavior, though it doesn't discuss pagination or rate limits.

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 with no filler. The first sentence front-loads the action and output metrics, while the second clarifies the analytical purpose. Every word earns its place.

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 read-only aggregate query with strong annotations and full schema coverage, the description names the outputs and intent clearly. It could add a brief pointer to sibling tools or note that it returns only site-level aggregates, but nothing critical 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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description's mention of 'optional goal revenue' loosely maps to include_revenue and goal but adds no new semantic detail beyond the schema.

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 uses a specific verb ('Return'), names the resource ('a Plausible site'), and lists concrete metrics (visitors, visits, pageviews, bounce rate, visit duration). The word 'aggregate' and the tool name distinguish it from sibling tools like plausible_breakdown and plausible_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?

No explicit guidance is given about when to use this tool versus alternatives. It does not mention plausible_breakdown for dimension-level data or plausible_timeseries for trend data, so the agent must infer usage from the name and sibling names.

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