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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real interpretive context beyond the structured data: it enumerates the specific states returned and warns that missing purchase history is not proof that purchase tracking is unconfigured.

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?

Three tight sentences: what it returns first, then the disambiguation to value tools, then the interpretation caveat. Every sentence carries distinct information with no padding.

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 zero-parameter, output-schema-backed metadata tool, the description covers scope, alternative routing, returned state vocabulary, and a key interpretive pitfall. No gaps an agent would need to fill before calling it.

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?

The tool takes zero parameters, so per the rubric the baseline is 4. Nothing in the description is needed to clarify parameter usage, and nothing misleading is added.

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 concrete verb and resource (returns RevenueScope metric definitions, measurement boundaries, state meanings) and explicitly contrasts itself with value-returning tools. An agent can distinguish this metadata-only tool from every analytics sibling without opening a schema.

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?

Explicitly says to use a site-scoped analytics tool for values, establishing when this tool is the wrong choice. It does not name a specific sibling (e.g. get_summary, get_breakdown), so routing is slightly less precise than it could be.

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