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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false), so the bar is lower. The description adds real value beyond that by explaining the semantics of the returned item fields (numerator_scope's three possible slices, denominator_metric, denominator_scale values), which compensates for the absent output schema.

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 dense sentences with the core purpose front-loaded; the ratio definition, field semantics, and the follow-up tool are packed without filler. Every clause earns its place given there is no output schema to lean on.

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-argument list tool with no output schema, the description covers purpose, item field semantics, and the natural next step (query_unit_costs). It only omits secondary details such as volume, ordering, or pagination behavior, which are minor here.

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. The field descriptions in the text refer to response items rather than inputs, so there is no parameter ambiguity to resolve.

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 and resource ("List this account's saved unit-economics metrics") and immediately defines the resource with concrete examples ("cost per 1k API requests", "infra cost per order"). It distinguishes itself from query_unit_costs, which computes a single metric, by positioning this tool as the catalog of saved definitions.

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?

Gives clear context: use this to enumerate saved metrics, then "Pass an id to query_unit_costs to compute one over a date range," and points to list_cost_tags for tag_value scope values. It does not state explicit exclusions or why to choose this over, say, list_cost_sources, so it stops short of a 5.

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