Get one dataset

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is fully covered. The description adds value beyond annotations by disclosing the aggregation behavior — that one call returns contact point, coverage, documentation links, and every distribution with its licence — a trait an agent cannot infer from the schema or annotations alone.

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 first sentence front-loads the purpose, the second sentence earns its place by explaining why aggregation matters, and the Args block is cleanly formatted. The phrasing 'answers «who publishes it, through which interface, under which licence»' is slightly florid but not filler, so the size is justified.

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 an output schema documenting return structure, annotations covering safety and idempotency, and both parameters explained including value provenance, nothing an agent needs to call this tool correctly is missing. The workflow context (used after search_catalog or list_datasets) completes the picture.

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 carries the full burden for both parameters. It delivers: dataset_id gains provenance (a UUID from search_catalog or list_datasets) which the schema's pattern constraint lacks, and language gains a semantic effect (titles and descriptions). Both parameters are meaningfully enriched.

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?

Opens with a specific verb+resource ('Retrieve the full, aggregated metadata record for one dataset') and immediately positions itself as the aggregated detail tool. The distinction from sibling get_dataset_distributions is clear: this tool returns everything in one call, so an agent can tell them apart without opening either schema.

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?

Describes the intended workflow (search_catalog → get_dataset) and states where dataset_id comes from (search_catalog or list_datasets). It explicitly contrasts with 'a separate distributions or contact lookup,' steering agents away from redundant sibling calls. It never names get_dataset_distributions explicitly or states hard when-not-to-use conditions, 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.