describe_indicator_dimensions

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

The description discloses that the tool operates 'by sampling' and caps the sample size at 1000, providing insight into its behavior. However, it does not clarify other aspects like idempotency, error handling, or schema of the output, and no annotations are present to supplement.

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 extremely concise: two sentences covering purpose and parameters with no filler. The information is front-loaded and efficiently structured, earning its place in every part.

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 output schema exists, the description appropriately focuses on tool behavior and parameters. It covers the sampling methodology and parameter constraints, but could briefly mention what the output contains (e.g., list of dimension names) to fully complete 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?

The description adds meaningful context to both parameters: indicator_code with an example format and sample_size with default and upper limit. This compensates for the 0% schema description coverage by clarifying usage and constraints.

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 that the tool 'describe which disaggregations (Dim1, Dim2) an indicator uses, by sampling.' It provides a specific example indicator code and differentiates from siblings like list_dimensions or get_indicator_metadata, making the purpose unambiguous.

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention scenarios or comparisons with sibling tools such as get_dimension_values or get_indicator_data, leaving the agent to infer usage context.

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