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

No annotations are provided, so the description carries the burden. It signals a pure read ('Read this') over a zero-parameter metadata resource, which is effectively transparent about the safety profile, but it says nothing about failure modes or whether the metadata is stable/cached. For a trivial metadata read this is sufficient, but not rich.

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?

Two short sentences, front-loaded with the returned fields, with the usage note last. No filler, no restatement of the tool name or title.

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?

With no output schema, the description must convey the return shape, and it does so by listing source, date, licence, and citation. That is close to complete for a zero-arg metadata tool; only the exact field names and format remain unspecified.

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 takes zero parameters, so the baseline is 4. The description appropriately does not invent parameter detail, instead describing the returned content, which is the only semantic surface available.

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 enumerates exactly what the tool returns (source, compute date, licence, citation) and names the specific dataset it belongs to, so an agent can distinguish it from dataset_columns, dataset_stats, and the other dataset_* read tools. It lacks a verb, but the noun-level specificity is enough to identify the resource unambiguously.

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?

"Read this to attribute a figure correctly" gives an implied use case (citation/attribution), which is more than nothing. But it offers no explicit when-not guidance and never names a sibling as the alternative for other metadata needs. Adequate but thin.

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