list_dimension_values

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

Annotations already declare a safe read-only, idempotent, non-destructive operation, so the safety profile is covered. The description adds that it returns the *actual* observed values rather than a theoretical list, which is useful, but says nothing about the time window, result caps, or pagination.

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?

A single tight sentence with the core behavior front-loaded and examples in parentheses. No filler.

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?

With an output schema present, return values need not be described, and the annotations carry the safety profile. The description is nearly sufficient for a 2-param discovery tool, though the time-window semantics of 'days' remain unaddressed.

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 gives meaning to 'dimension' via concrete examples, but the 'days' parameter (time window, default 30) is never explained, leaving half the parameters undocumented.

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?

States a specific verb ('discover which values') and resource ('a dimension'), with concrete examples ('real page paths, real UTM sources') that make the output shape clear. It is distinguishable from list_metrics in spirit, though it never names a sibling to sharpen the contrast.

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 examples imply the use case (figuring out which concrete values exist before querying), but there is no explicit when-to-use, no exclusions, and no pointer to alternatives like breakdown or query_metrics. Usage is left to inference.

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