explain_metric

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

With no annotations, the description carries the behavioral burden. It clearly discloses that the tool never returns a current figure and that it returns definition, methodology, and caveats. It does not explicitly state read-only behavior, but the described purpose makes side effects highly unlikely.

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 dense but efficient, with each sentence serving a purpose: what the tool returns, when to use it, and where stat_key originates. It is slightly over-worded with parenthetical examples, but it remains well organized and front-loaded.

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's simplicity, an output schema exists, and the description covers purpose, usage boundaries, and stat_key provenance, it is mostly complete. The main gap is the unclear relationship between the country and stat_key parameters, which could confuse an agent despite the helpful examples.

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 explains where stat_key comes from (query_data for the same country) and hints at country values via 'ireland'/'united-kingdom', but it does not clearly define the country parameter or enumerate allowed values. The 'ONE stat' phrasing combined with country examples is somewhat ambiguous.

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 states the tool provides definition, methodology, and standing caveats for one statistic, and explicitly contrasts this with current-value lookups handled by query_data/compare. This makes the resource, scope, and non-current-value nature clear.

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 explicit when-to-use guidance (questions about what a metric means or how it is measured), explicit when-not-to-use guidance (specific area values), and names the sibling tools query_data/compare as the alternatives. It also tells the agent where stat_key comes from, leaving little to inference.

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