resolve_metric

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), and the description adds real behavior beyond them: ambiguity is returned with candidates, a mismatched measure is refused with a reason, 'nothing is guessed,' and per-source disagreement cannot be observed in the file. These failure and limitation disclosures are exactly what an agent needs before relying on the result.

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?

Front-loaded with the core lookup behavior and every sentence carries information, but the paragraph is dense and long, packing aliases, ambiguity, conversion, sibling behavior and the merge caveat into one block with little structural separation. Slightly harder to scan than it needs to be.

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?

There is no output schema, so the description carries the return-shape burden and does it: canonical metric, unit, unit variants with conversion formulas, and whether the export holds the metric. Combined with the refusal and ambiguity semantics, an agent has everything required to invoke and interpret the tool.

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 100%, so the baseline is 3, but the description adds meaning the schema does not: percentages come back as 0-to-1 fractions, the {value, unit} pairing triggers a single-value conversion, and unit variants include the exact conversion formula. That is genuine semantic enrichment on top of an already-documented schema.

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 ('look a metric name up in the canonical naming and unit table') and enumerates the accepted input forms and what is returned, so it is unmistakable next to list_metrics or get_health_metrics. It also carves out its conversion role, distinguishing it from the data-fetching siblings.

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 tells the agent which siblings accept the same aliases (get_health_metrics, get_trends, compare_periods, get_structured_export, correlate_metrics) and that 'other tools take the export's own names,' plus when to use the {value, unit} conversion form. When-to-use and when-not-to-use are both covered.

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