dataset_versions

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It notably reveals that the portal itself hides unchanged versions, while this tool marks them—critical context for interpreting results. It does not mention any side effects or read-only status, but for a version-history tool this is implied and not a significant gap.

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: it leads with the core purpose, highlights an important nuance, and ends with an argument reference. It avoids unnecessary details and is easy to scan, though the 'Args' section slightly overlaps with the schema.

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 a single parameter and the existence of an output schema, the description covers the essential context: what the tool does, how to call it, and a key interpretation detail about unchanged content. It does not explain return value structure, but that is presumably handled by the output schema, so overall it is 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 provides no description for the `dataset` parameter, so the tool description must compensate. It does so by giving a concrete example (7707082071-ksrms) and pointing to list_datasets for obtaining valid identifiers. This is sufficient for basic usage, though it could elaborate on format or constraints.

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 indicates the tool provides version history for a single dataset, including publication timing and content-change status. It distinguishes itself from sibling tools by focusing on version-level details rather than dataset listings or salary metrics. However, it lacks an explicit action verb like 'returns' or 'shows,' making it slightly less directive.

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 implicitly guides usage by requiring a dataset identifier and referencing list_datasets as the source for valid IDs. It also explains a unique scenario where the tool adds value (detecting versions with unchanged content), which helps the agent decide when to use it. It does not explicitly name alternatives, but the sibling tools are clearly distinct in purpose.

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