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

With no annotations, the description carries the full behavioral disclosure burden. It lists the returned content fields and implies a read-only, side-effect-free operation. It could be more explicit about output format, but the metadata-only nature is clearly conveyed.

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: the first names the returned fields, the second gives the usage context. It is front-loaded with the essential content and contains no filler.

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 retrieval tool with no output schema, the description is sufficient. It tells the agent what it will receive (source, date, licence, citation) and why to call it (attribution), so no critical information is missing for selection and invocation.

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, so there is nothing to document; the baseline of 4 applies. The description adds no parameter information, but none is needed.

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 identifies exactly what the tool exposes — source, computed date, licence, and citation — for the OrderPadLedger dataset, and explicitly frames it as a read action for attribution. This clearly distinguishes it from sibling data-access tools like dataset_search or dataset_row, which return dataset contents rather than provenance metadata.

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 instruction 'Read this to attribute a figure correctly' provides a clear use case: when the agent needs citation or provenance information. It does not explicitly name alternatives or state when not to use it, but the context is strong enough that an agent can infer this tool is for metadata, not data operations.

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