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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive). The description adds the genuinely useful fact that the return payload is dataset cards rather than statistics values, which matters because there is no output schema to disclose the 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?

Three short sentences with zero filler, front-loading the purpose, then the workflow position, then the return-shape caveat.

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?

With no output schema, the 'dataset cards, not values' line carries important weight and the filter behavior is clear. Minor omissions remain, such as how query/null results or the limit cap affect an agent's next step.

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 100%, so the schema already documents query, country, source_name and limit. The description repeats that country and source are optional filters without adding format or matching semantics, so the baseline 3 applies.

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 ('Find official labour market datasets') and enumerates the source domains covered. It also distinguishes itself from siblings by noting it returns dataset ids rather than values.

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?

'Use first to get dataset ids' gives a clear sequencing cue relative to get_dataset/get_series/inspect_dataset, and 'Returns dataset cards, not values' tells the agent when this tool is insufficient. No sibling is named explicitly, so it stops just short of a 5.

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