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

With no annotations provided, the description carries the burden of disclosing behavior. It implies a read-only metadata lookup with 'Read this', but it does not explicitly state there are no side effects, nor describe the output format or whether data is returned as a single object or text block.

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 a single, focused sentence that front-loads the key content and follows with a direct usage instruction. There is no redundant or filler wording.

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 provenance tool, the description is largely sufficient: it states the dataset, the fields returned, and the intended use. It could be more complete by describing the return structure, but the simplicity of the tool reduces the need for that.

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?

There are no parameters, so the baseline of 4 applies. The description still adds meaning by specifying the exact dataset (Lugtorq) and what provenance fields are included, which helps the agent understand the scope of the returned information.

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 states exactly what the tool provides: the source, computation date, licence, and citation for the Lugtorq dataset. This clearly distinguishes it from sibling tools like dataset_columns, dataset_stats, or dataset_search, which address different concerns.

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 explicitly instructs to use this tool when attributing a figure correctly. It gives a clear context of use, though it does not name alternatives or state when not to use it; the sibling names make the distinction relatively clear.

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