get_dataset

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

Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context about the aggregated nature of the response, including what data fields are returned and how the language parameter affects titles/descriptions. It does not mention error handling or null results, but the annotation coverage lowers the bar.

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 front-loaded with the primary action, followed by a useful but slightly verbose workflow explanation. All sentences contribute value, but the second paragraph about the two-call workflow could be tightened. Overall, it is well-structured and appropriately sized.

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?

The description is comprehensive for the tool's complexity: it explains the aggregated nature, parameter provenance, and relationship to sibling tools. Since an output schema exists, return value details are not required in the description. With strong annotations and clear usage guidelines, this is a complete package.

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 `dataset_id` as a UUID from sibling tools and `language` as the language for titles/descriptions, which adds meaningful semantics beyond the schema's enumerated values and pattern. Minor gap: no detail on fallback behavior if language is not supported.

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 identifies the tool as retrieving the full aggregated metadata record for one dataset, with specific details (contact point, coverage, documentation, distributions with licences). It distinguishes itself from sibling tools by explaining this is the aggregated detail tool that avoids separate lookups.

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?

The description explicitly recommends a workflow: `search_catalog` → `get_dataset` answers key questions in two calls, and states that no separate distributions or contact lookup is needed. It also instructs that `dataset_id` should come from `search_catalog` or `list_datasets`, providing clear when-to-use guidance.

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