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

With no annotations supplied, the description carries the full behavioral burden, and it does disclose the four content categories returned (source, date, licence, citation). The read-only nature is implicit in a zero-parameter metadata lookup, but the description says nothing about return format or whether the dataset identifier is fixed.

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, front-loaded with the returned content and closed with the actionable use case. No filler and nothing that could be trimmed.

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?

Absent an output schema, the description reasonably compensates by listing the fields a caller receives, plus the purpose for reading them. It could be slightly more complete by hinting at the response shape or whether the date is ISO-formatted, but for a trivial zero-param metadata tool this is nearly sufficient.

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 is nothing for the description to disambiguate; the baseline of 4 applies. The description correctly implies no input is needed, mentioning only the dataset it operates on.

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 a specific resource (the dataset's provenance metadata) and enumerates exactly what it returns: source, computation date, licence, and citation. This is clearly distinguishable from the sibling tools, which deal with columns, rows, stats, and search rather than attribution 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?

"Read this to attribute a figure correctly" gives a concrete triggering condition for using the tool. It stops short of naming alternatives or stating when not to bother (e.g., when no citation is needed), so it falls just short of fully explicit routing.

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