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

No annotations are provided, so the description carries the burden. It discloses that this is a read-only metadata lookup by framing it as something to 'read' and by describing static provenance content, not a mutation or computation. It could say more about whether it is always available or how the data is refreshed, but for a zero-parameter informational tool it is reasonably transparent.

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 tight sentences, front-loaded with the returned fields and closing with the purpose. Nothing is redundant or misleading.

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 no-input, no-output-schema metadata tool, the description covers what the agent needs: what it returns and why to call it. No additional behavioral details are necessary for correct invocation.

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?

Zero parameters, so the baseline is 4. The description correctly implies no filtering or input is needed by presenting the tool as a fixed read of dataset-level metadata.

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 states a specific resource (Bank Code Lookup dataset) and enumerates exactly what it returns: source, computation date, licence, and citation. It is clearly differentiated from siblings like dataset_stats or dataset_columns, which provide analytical or structural views rather than provenance.

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?

It gives a clear condition for use: 'Read this to attribute a figure correctly.' That tells the agent when this tool is the right choice, though it does not explicitly name alternatives or exclusions.

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