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

No annotations are provided, so the description carries the behavioral disclosure burden. It transparently enumerates the returned content—source, computed date, licence, citation—making clear this is a metadata/read operation. It does not describe output formatting, but for a simple provenance lookup the disclosure is adequate.

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 two concise sentences with no filler. The first sentence states exactly what information is returned, and the second gives the practical use case. The title reinforces the purpose without 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?

For a zero-parameter metadata lookup tool, the description is complete. It names the dataset, lists the provenance fields, and gives the calling context. No output schema exists, but the return values are described well enough for an agent to use the tool correctly.

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 tool has zero parameters, and the baseline for such tools is 4. The description does not need to explain parameter meanings, and it does not attempt to. Nothing about parameter semantics is missing.

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 what the tool provides: the source, computed date, licence, and citation for the Enrolvo dataset. It also explains the intended purpose—attributing a figure correctly—which distinguishes it from sibling data-exploration tools like dataset_stats or dataset_search.

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 gives a clear usage context: 'Read this to attribute a figure correctly.' This tells the agent when the tool is relevant, though it does not explicitly exclude alternatives. Given the sibling tools are all about querying dataset contents, the attribution use case is sufficiently distinct.

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