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

With no annotations, the description carries the behavioral disclosure burden. It indicates an informational read operation and lists the returned content, but does not mention format, whether the dataset name is fixed, or any other operational details. For a parameterless lookup this is acceptable but not rich.

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 key content list and followed by a practical usage cue. Every word earns its place; no redundancy or 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-argument metadata lookup with no output schema, the description is complete: it names the dataset, the kind of data returned, and the intended use case. Nothing else is required to invoke it successfully.

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 and all schema coverage is effectively complete non-information, so no parameter explanation is needed. The description still contributes by naming the specific pieces of provenance returned.

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 distinct resource ('the GPA Grade Compare dataset') and precisely states what the tool provides: source, computed date, licence, and citation. This clearly separates it from dataset_columns, dataset_stats, and the other sibling tools.

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 final sentence, 'Read this to attribute a figure correctly,' tells the agent when to use the tool. It does not explicitly list when not to use it or name alternatives, but the context is clear enough for a zero-parameter provenance lookup.

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