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

No annotations are provided, so the description carries the full burden. It discloses the shape of the payload (source, date, licence, citation), which tells the agent this is a static metadata read with no mutation risk, but it never explicitly states that the call is side-effect free or that it requires no arguments. Adequate, with a small 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 short sentences with zero filler. The inventory of returned content is front-loaded and the usage instruction follows immediately, so an agent gets both the payload and the trigger in one pass.

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 compensates by listing the fields the caller will receive (source, date, licence, citation). For a zero-argument metadata lookup this is essentially complete, though it stops short of describing the format of those fields (e.g. machine-readable vs. prose).

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 schema has zero parameters, so there is nothing for the description to disambiguate; the baseline for a 0-param tool is 4. No parameter information 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 specific resource (provenance of the Lettza dataset) and enumerates the returned content: source, computation date, licence, citation. That clearly separates it from siblings like dataset_stats or dataset_row, which deal with values rather than attribution metadata. It is not a tautology of the title, though the verb is implied rather than stated.

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 explicit trigger condition for using the tool. No competing alternative is named, but the attribution use case is narrow enough that no sibling is a plausible substitute, so the guidance is sufficient.

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