describe_dataset

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

With no annotations provided, the description carries the full burden. It explains the return structure (DatasetDetail with fields) and implies a read-only operation. While it doesn't explicitly state non-destructiveness, the content is transparent enough.

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 brief introductory sentence followed by a bulleted list of return fields. Every sentence adds value, with no wasted words.

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 no annotations and a single parameter, the description is highly complete. It explains the tool's purpose, usage context, and return structure. An output schema exists, but the description still provides useful details.

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 100%, so baseline is 3. The description adds value by recommending predecessor calls (search_datasets) for discovering dataset_id, enhancing understanding beyond the schema.

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, returnable measures, units, and source. It uses specific verbs and resources ('Describe a dataset's...') and distinguishes itself from siblings by noting it should be used 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 explicitly advises using this tool before calling get_data on a new dataset, providing clear usage context. It does not explicitly state when not to use or mention alternatives beyond get_data, but the guidance is strong.

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