describe_dataset

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

No annotations exist, so the description must be self-contained. It lists the return fields (id, name, description, filters, units, source URL, example invocation strings) and includes an example with return structure. No behavioral contradictions.

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: one sentence for purpose, followed by an example and return format. No redundant information; each sentence adds value.

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 tool's simplicity (one parameter, output schema present), the description covers all essential information: what it does, how to use it, and what it returns. No gaps.

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 already provides comprehensive details (100% coverage) for the single parameter, including examples and case-insensitivity note. The description does not add extra parameter semantics beyond mentioning the dataset types, which is not necessary.

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 starts with 'Describe one NEM dataset — schema, filters, cadence, source URL' which clearly states the action and resource. It differentiates from sibling tools like search_datasets and list_curated by focusing on a single dataset's 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 advises using search or list-curated endpoints to discover dataset IDs before calling this tool. This gives clear guidance on the proper workflow, though it does not explicitly state when not to use it.

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