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

No annotations exist, so the description carries the full burden, but the tool is a zero-parameter metadata lookup whose behavior is inherently low-risk. It discloses the content of the response but says nothing about whether the result is stable, cached, or authoritative for a specific release, leaving a modest 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?

Two sentences, no filler, with the returned field list front-loaded ahead of the usage instruction. Every clause carries information an agent needs.

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 effectively doubles as return-value documentation by enumerating source, date, licence, and citation. It is nearly complete for such a simple tool, though it does not describe the shape or format of those fields.

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 no parameters, so there is nothing for the description to disambiguate; baseline is 4 per the zero-parameter rule. No parameter-level detail is needed or 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 names the exact resource and enumerates what it returns (source, computation date, licence, citation) for a named dataset, which is far more specific than a restatement of the title. It does not explicitly contrast itself with siblings like dataset_stats or dataset_columns, but the metadata-vs-data distinction is obvious from the field list.

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 a clear triggering condition for when to call the tool. It stops short of naming alternatives or stating when this tool is unnecessary (e.g. for raw values), so it is clear context without exclusions.

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