rank_areas

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

With no annotations, the description carries the full behavioral burden and does substantial work: it discloses that an unregistered level returns an empty list rather than an error, that rate_per_1000 is the correct ranking value for denominator-less stats, and that caveats must be carried forward. It does not mention sort order, but the output schema covers return shape.

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 long but every sentence earns its place: core purpose first, then parameter sourcing, then level behavior, then the critical denominator caveat and caveat-propagation rule. There is no filler or repetition of schema defaults.

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 complexity, the zero annotations, and the low schema coverage, the description covers the non-obvious edge cases an agent needs: country-specific stat keys, invalid level behavior, denominator-less stat handling, and caveat propagation. An output schema exists for return shape, so the description is complete enough to call the tool 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 coverage is 0%, and the description fully compensates. It explains how to source stat_key, clarifies that country must match the stat_key's country, and gives detailed semantics for level, including default behavior and compatible_levels. Even the special rate_per_1000-vs-latest_value distinction adds meaning beyond the raw 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?

The description opens with a precise verb-resource-scope statement: 'Rank every area at one geography level by its latest figure for one stat, for one country.' Concrete examples ('which counties have the highest median sale price') make the operation unmistakable and clearly distinct from sibling tools like list_areas or list_datasets.

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 strong contextual guidance: stat_key must come from list_datasets for the same country, level must be omitted or come from compatible_levels, and country-specific stat differences are called out. It does not explicitly state 'use this instead of X' versus siblings, but the usage context is clear enough to route an agent correctly.

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