explain_metric

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

With no annotations, the description carries the full burden, and it delivers: it explicitly states the tool never returns a current figure and is limited to one stat at a time. It also communicates the dependency between stat_key and country via list_datasets. This is clear behavioral disclosure beyond what the schema provides.

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 dense sentences with no filler. The core definition is front-loaded, the when-to-use guidance follows, and the parameter provenance is stated at the end. Every sentence earns its place.

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 two-parameter tool with an output schema, the description is complete: it explains what the tool returns, what it never returns, when to use it, and how to obtain the required stat_key. Nothing essential is missing for an agent to invoke it correctly.

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 partially does: it indicates the country values ('ireland' or 'united-kingdom') and explains that stat_key comes from list_datasets(country=...) for the same country. It stops short of listing possible stat_key values or formats, but the source guidance is strong.

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 clearly states the tool's purpose: provide definition, methodology, and caveats for one metric, not current values. It also distinguishes itself from get_dataset_for_area and rank_areas, which answer area-value questions. This makes the tool's role unambiguous.

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 explicitly says when to call: when the question is about what a metric means or how it is measured, and when not to call by naming the siblings that already answer value queries. It also tells the agent where to source stat_key from, list_datasets(country=...) for the same country.

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