describe_indicator_dimensions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does reveal that the tool works 'by sampling' and that sample_size is 'capped at 1000', which is useful behavioral context beyond the raw schema. However, it does not disclose potential inaccuracy from sampling, permission requirements, or side effects, which is a meaningful gap for a data-sampling tool.

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 two compact sections: a single-purpose sentence followed by terse parameter explanations. Every word earns its place—no fluff, examples are concrete, and the core action is front-loaded. This is excellent structure for an agent to parse quickly.

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?

Given the tool has an output schema, the description does not need to explain return values. It covers the main action and parameters adequately. However, it could add context about what 'uses' means (e.g., presence of the disaggregation in the indicator's data) and note that sampling may miss rare disaggregations. These are minor gaps but prevent a higher score.

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 does this effectively: for indicator_code it provides a concrete example ('WHOSIS_000001'), and for sample_size it explains the meaning ('Rows to sample'), the default (200), and adds the cap (1000) not present in the schema. This adds significant value over the raw input schema, though it could elaborate on the format of indicator_code or the concept of disaggregations.

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 states a specific action and resource: 'Describe which disaggregations (Dim1, Dim2, Dim3) an indicator uses, by sampling.' This differentiates from siblings like list_dimensions (lists all dimensions) and get_dimension_values (retrieves values for a dimension) by focusing on which disaggregations an indicator uses. However, it doesn't explicitly name these alternatives, so it's not a 5.

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 provides no explicit guidance on when to use this tool versus the sibling tools, nor any when-not conditions. The purpose statement implies usage (when you need to know which disaggregations an indicator uses), but there are no exclusions or alternative tool references. This leaves the agent to infer the selection logic from the purpose alone.

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