get_dataset_dimensions

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

With no annotations provided, the description carries the behavioral disclosure burden. It clearly explains that the tool returns dimension concepts and geography type vintages, and it adds practical behavioral context about filter validity and downstream usage. It does not explicitly state auth requirements or side effects, but for a read-only listing tool the described behavior is sufficiently transparent.

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 well-organized and front-loaded with the core purpose. Each sentence earns its place: the essential prerequisite warning, the variability context, and the geography_types explanation with concrete examples. It is moderately long but dense with useful guidance, not redundant filler.

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 tool has an output schema, so return values are already documented. The description covers why the tool matters, how it relates to fetch_data and search_codes, and what the geographic_types values mean. For a discovery tool with one parameter, this is complete and actionable.

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% for the single parameter, dataset_id, which is already described in the schema with an example. The description reinforces the importance of the dataset but does not add significant new parameter-level meaning beyond the schema. Baseline of 3 is appropriate because the schema handles parameter documentation.

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 opens with a specific verb and resource: 'List the dimensions a dataset accepts as filters, and its geography types.' It clearly distinguishes this from siblings like fetch_data and search_codes by describing the unique role of dimension discovery. It also gives concrete examples of what the output contains, leaving no ambiguity about the tool's purpose.

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 gives explicit when-to-use guidance: 'Essential before fetching.' It explains that dimensions vary per dataset and that unlisted filters will be rejected or silently ignored by fetch_data, making the prerequisite relationship clear. It also names the next step, passing the chosen geography type to search_codes, which routes the agent correctly among sibling tools.

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