list_datasets

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

No annotations are provided, so the description carries the full burden. It discloses that Ireland and the UK track genuinely different datasets, warns to call for the right country, and clarifies that this returns stat_key for rank_areas. It does not mention pagination, access permissions, or error behavior, but for a read-only listing operation the key behavioral caveat is present. Slightly more detail could lift it to 5.

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 sentences with the purpose front-loaded in the first line. Each sentence earns its place: the first states the core action, the second adds the country-specific caveat, and the third routes to sibling tools. It is slightly verbose but efficient and structured.

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 a single optional parameter, an output schema present, and no annotations, the description covers the allowed parameter values, the key behavioral caveat (country specificity), and routing to sibling tools. No critical information needed to invoke it correctly is missing.

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?

The input schema has zero parameter descriptions (0% coverage) and only a default value for 'country'. The description compensates fully by explicitly enumerating the two allowed values ('ireland' or 'united-kingdom') and explaining the country-dependent dataset variation, which is essential semantic information not present in the 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 clearly states the action ('List every dataset') and the resource (datasets/stat stats StatsMapped tracks for a country), including the output fields (key, human label, geography levels). It explicitly differentiates from sibling list_area_datasets by noting that get_dataset_for_area requires list_area_datasets instead, so an agent can tell tools apart without opening schemas.

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 provides explicit when-to-use guidance: 'Call this first to find the right stat_key for rank_areas' and when-not-to-use guidance: 'for get_dataset_for_area, use list_area_datasets instead'. It also warns to call for the correct country before assuming a stat_key exists, covering the main conditional use case.

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