query_metric

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

With no annotations, the description carries the full burden and does so thoroughly. It discloses that invalid dimensions are refused rather than approximated, that replies carry a result_id that must be verified, and that ratios include numerator/denominator so aggregations must use sums, not averages. These are non-obvious behavioral expectations.

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: purpose first, then month semantics, then dimension constraint, then verification and rollup rules. Every sentence adds operational value; there is no filler or repetition of schema content.

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?

Given the tool complexity, absence of annotations, and presence of an output schema, the description covers the essential operational details: inclusive date range, dimension validation, the result_id/verify_answer contract, and correct ratio aggregation. An agent has enough to invoke query_metric correctly and integrate its result into a response.

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 coverage is 0%, so the description must compensate. It explains start_month/end_month as 'YYYY-MM' with inclusive bounds, and dimension as restricted to values listed for the metric, with refusal behavior. metric_id is less elaborated, but its role as the metric selector is inferable from the primary sentence and sibling tools.

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 and resource: 'Compute a metric over a range of months, optionally cut by one dimension.' This clearly distinguishes query_metric from siblings like list_metrics (listing metrics) and get_metric (likely retrieving metadata), and from verify_answer (checking results).

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?

It gives clear invocation context: month format, inclusive ranges, dimension validation, and the required hand-off to verify_answer. It does not explicitly state when not to use this tool or name alternatives, but the usage flow is strongly implied by the sibling names and the verification instruction.

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