describe_dataset

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

No annotations provided, so description carries full burden. It describes the return value in detail (DatasetDetail with fields) and implies it is a read-only introspection. However, it doesn't explicitly state idempotency or lack of side effects, but the description is sufficient for this simple tool.

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?

Description is concise: two paragraphs. First sentence states purpose, second gives usage guidance, third details return values. No unnecessary words, well-structured.

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 simple one-parameter tool and existence of an output schema, the description is fairly complete. It explains the return fields (dimensions, measures, source_url, etc.) and usage context. Could mention safety but not necessary.

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 coverage is 100% with examples and description. The description adds context by explaining that the parameter identifies a curated dataset and that the result helps inform subsequent get_data calls. This goes beyond the schema's basic description.

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?

Clearly states 'Describe a dataset's filterable dimensions, returnable measures, units, and source.' Uses specific verb 'describe' and resource 'dataset', and distinguishes from siblings like 'get_data' by stating 'Use this before calling get_data on a new dataset'.

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?

Explicitly tells when to use: 'Use this before calling get_data on a new dataset'. Also explains what it provides (valid filter keys, values, measure aliases, source URL). Doesn't explicitly state when not to use, but the guidance is clear.

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