describe_dataset

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

No annotations are provided, so the description carries the full burden. It lists the return fields but does not explicitly state that the tool is read-only or idempotent, nor does it mention auth requirements or side effects. However, the description implies safe discovery behavior, achieving a minimum viable score.

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 concise and well-structured: a lead sentence with the core purpose, a usage note, and a clear list of return fields. Every sentence adds value with no redundancy.

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 low complexity (single parameter, no nested objects) and the presence of an output schema, the description is complete. It covers the tool's role, parameter discovery, and return structure.

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 input schema has 100% description coverage for the single parameter. The description adds value by explaining how to discover valid dataset IDs and noting case-insensitivity, going beyond the schema's basic definition.

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's purpose: to describe a dataset's filterable dimensions, measures, units, and source. It uses specific verbs and resource references, and explicitly differentiates itself from sibling tools by recommending its use before get_data.

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 explicit usage guidance: 'Use this before calling get_data on a new dataset.' It explains what the tool returns and its role in discovery. It does not explicitly state when not to use it, but the context implies its appropriate use.

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