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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. Beyond that, the description discloses what the call actually yields (title, period, source, dimensions with codes and labels), which is genuinely useful since there is no output schema; it does not mention error behavior for unknown dataset ids, keeping it below 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?

It is a single compact sentence with no filler, and the payoff (the get_dataset_data filters) is front-loaded at the end of the clause. The trade-off is that it is a noun phrase with no verb, making the operation slightly less immediately parseable than a purely declarative sentence.

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?

For a one-parameter lookup with no output schema, the description does cover the returned fields and the workflow link to get_dataset_data. However, it does not say what happens when a dataset_id is invalid or what the dimension codes/labels structure looks like, which matters given the absence of an output schema.

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 100% and the single dataset_id parameter is already documented in the schema with an example (nsi-1169) and its provenance from search_datasets. The description adds no further meaning about the id format or lookup behavior, 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?

The description names the resource (a dataset's metadata: title, period, source, dimensions with codes and labels) and implicitly the operation (fetch that metadata). It distinguishes itself from the sibling get_dataset_data by framing itself as the source of that tool's filters, though it never uses an explicit verb like 'retrieve' or 'get'.

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 clear usage context: this is what you call to obtain the dimension codes and labels needed to build get_dataset_data filters, which effectively routes the agent from search_datasets to get_dataset to get_dataset_data. It does not state any when-not-to-use condition or an explicit alternative, so it stops 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.