get_cube_structure

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

Without annotations, the description carries full burden. It explains the output includes a 'path' field used as property URI in other tools, which is valuable behavioral context. However, it does not explicitly state read-only nature or absence of side effects, but being a 'get' operation implies safety.

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?

Two sentences: first defines purpose, second gives usage guidance and key output detail. Every sentence adds value, no redundancy. Front-loaded with core action.

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 single-parameter tool without output schema, the description fully explains its role in the workflow, what it returns, and how the output is used. No additional context needed for correct selection and invocation.

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 sole parameter cube_uri is described in schema as 'The URI of the cube (from list_cubes results)'. The description adds context that the URI comes from list_cubes, placing the parameter in the overall workflow. With 100% schema coverage, baseline is 3, but the extra guidance pushes it to 4.

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 the tool retrieves the structure of a data cube, specifying dimensions, measures, datatypes, and constraints. It distinguishes itself from sibling tools like get_dimension_values and query_observations by outlining its role as a preliminary discovery tool.

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 mandates calling this tool before query_observations to understand available dimensions and measures. It provides a clear directive on when to use it, effectively guiding the agent in workflow sequencing.

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