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

With no annotations provided, the description carries the burden of disclosure. It clearly indicates this is a read-only metadata retrieval tool ('Read this') and enumerates the content returned: source, date computed, licence, and citation. This is sufficient for a simple zero-parameter tool, though it does not discuss formatting or edge cases.

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?

The description is a single, well-structured sentence that front-loads the tool's content and concludes with a clear usage directive. Every phrase earns its place with no redundancy.

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-parameter, metadata-only tool, the description is fully complete: it names the dataset, lists the exact provenance fields, and tells the user when to consult it. An agent has everything needed to invoke this tool correctly.

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 the schema already reflects that with 100% coverage. Per the rubric, the baseline is 4 when there are no parameters, and the description adds contextual meaning about what the returned provenance metadata contains.

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 (FlatRateBook dataset) and details exactly what the tool provides: source, computation date, licence, and citation. The title reinforces the purpose. This clearly distinguishes it from the sibling data-manipulation tools like dataset_search and dataset_stats.

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 description states when to use it: 'Read this to attribute a figure correctly.' This gives a clear use case and implies it is for citation/attribution rather than data exploration. However, it does not explicitly name alternatives or state when not to use it, though sibling names make the distinction fairly obvious.

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