Plausible: breakdown by dimension

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: pagination behavior (next_offset), the session-metrics restriction, and the meaning of 'percentage'. It doesn't describe rate limits or error behavior, but for a read-only analytics query tool, the disclosed constraints are sufficient.

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 compact and front-loaded: the first sentence states the core purpose, followed by four illustrative examples and two critical rules. Every sentence earns its place. The examples are formatted as JSON snippets that an agent can directly adapt.

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 analytics tool with 100% schema coverage and no output schema, the description covers the essential operational knowledge: what dimensions/metrics are available, how pagination works, and a key constraint. It doesn't mention response format details (e.g., markdown vs json) but the schema covers response_format. The only minor gap is not explaining what happens when no metrics are specified, though the schema default covers that.

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 already documents all 10 parameters thoroughly. The description adds value by showing realistic parameter combinations in examples (e.g., filters with goal dimension, metrics arrays, offset for pagination) and by explaining the 'percentage' metric's meaning. This goes beyond the schema's enum list.

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 opens with a specific verb ('Rank groups by metrics') and enumerates the exact dimensions it supports (top pages, sources, countries, devices, UTM campaigns, goals, custom properties). This clearly distinguishes it from sibling tools like plausible_get_aggregate (which returns totals) and plausible_get_timeseries (which returns time-series data). The title and description align well.

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 provides concrete examples for common use cases (top sources, top pages with engagement, goal conversions by campaign) and explicitly explains pagination via next_offset. It also states a critical rule about session metrics not being combinable with event dimensions other than event:page, which prevents invalid calls. While it doesn't explicitly name sibling tools, the examples and scope make the intended usage clear.

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