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

No annotations are supplied, so the description carries the burden; it does so by disclosing the four categories of information the call returns (source, computed date, licence, citation). It doesn't explicitly state that the operation is non-mutating, but it enumerates output content that would otherwise be unknown, which is meaningful added context for a zero-annotation tool.

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 no filler: the first enumerates the returned content and the second states the use case. Front-loaded with the payload the caller cares about.

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 and no annotations, the description must describe the return; it does so by listing the four fields an agent will receive. It omits the exact shape/format of the citation and licence values, but an agent has enough to call it correctly and interpret results.

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 there are no parameter semantics to explain; the baseline for a parameterless tool applies. Nothing in the description misrepresents what can be passed.

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?

Names the exact content returned — source, computation date, licence, citation — for a named dataset, and is immediately distinguishable from the sibling tools (dataset_columns, dataset_row, dataset_stats) that return data rather than metadata. An agent can tell what this tool is for without opening anything else.

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 condition for invoking it: whenever a figure needs citation or attribution. It does not state explicit exclusions or a contrasting sibling, but the usage context is concrete and unambiguous.

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