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

No annotations are provided, but this is a zero-parameter metadata read, so the behavioral surface is inherently small. The description discloses the fields the tool surfaces, which is useful, but says nothing about response shape, freshness guarantees, or any access constraints.

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 a list of the returned metadata followed by the use case. No filler, though the first sentence is a bare list rather than an action-oriented statement.

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 carries the burden of describing the return values, and it does enumerate them clearly for a zero-param tool. It is close to complete; only the response format and freshness/versioning behavior are unaddressed.

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 per the baseline there is no parameter semantics to explain. The description correctly avoids inventing arguments and instead spends its words on the returned content.

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?

States a specific resource and enumerates exactly what it returns (source, computation date, licence, citation) for a named dataset, which is far more concrete than its siblings' names alone. It does not explicitly contrast itself against siblings like dataset_columns or dataset_stats, but the resource is distinctive enough to be separable.

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 implied usage trigger, so an agent can infer it should be called when provenance/attribution metadata is needed. However, there are no exclusions and no named alternatives, so the guidance is contextual rather than explicit.

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