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

With no annotations, the description carries the full behavioral burden, and it does disclose the content returned (source/date/licence/citation). However, it says nothing about whether the metadata is static or cached, when it was last refreshed, or any doubt about the value beyond 'attribute a figure correctly'. For a zero-param metadata read the risk is low, so a middle score is fair.

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, no filler, with the returned fields front-loaded before the usage instruction. Every clause earns its place.

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?

There is no output schema, so the description must convey the return shape, and it does by enumerating the four returned fields. Combined with zero parameters and no annotations, this is nearly complete; only the absence of any note on freshness or stability of the metadata keeps it from a 5.

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. No parameter guidance is needed and none is misleadingly offered.

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 the specific resource it returns — source, computation date, licence and citation for the 'Send A Parcel Compare' dataset — so an agent knows exactly what payload to expect. It implicitly separates itself from the data-access siblings (dataset_columns, dataset_stats, dataset_row), which all return data rather than metadata, though it never names that contrast explicitly.

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 one concrete trigger for calling the tool, which is more than implied usage. But there is no guidance on when not to call it, no mention of prerequisites, and no routing to or away from any sibling tool.

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