get_dimensions

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

With no annotations, the description carries the full burden. It discloses the return type and structure ('dict[str, Any]: Table of dimensions and labels per dimension'), which is useful behavioral context beyond the schema. It clearly indicates a read-only operation, though it does not address error handling or the default empty dataset behavior.

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 a compact docstring with a one-line summary, an Args section, and a Returns section. No unnecessary words or repetition; it is well-structured and front-loaded.

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 simple two-parameter fetch tool with an output schema, the description covers the purpose, parameter roles, and return shape. It lacks explicit usage guidance and edge-case disclosure, but given the tool's simplicity, it is nearly complete.

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 schema lacks descriptions for both parameters (0% coverage). The description adds minimal semantics with 'catalog: Catalog identifier' and 'dataset: Dataset identifier,' but does not explain defaults, valid values, or how the parameters affect the returned dimensions. This partially compensates for the schema gap but remains thin.

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 'Fetch all dimensions for a dataset,' which is a specific verb+resource+scope statement. It clearly distinguishes from siblings like get_dimension_values (which fetches values of a single dimension) and get_metadata (which fetches metadata).

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 implies usage when a dataset's list of dimensions is needed, but it does not explicitly state when to prefer this tool over alternatives like get_dimension_values or query_datasets. No alternatives are mentioned, so guidance is only implied rather than explicit.

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