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

No annotations are supplied, so the description carries the burden, and it does disclose the exact payload (columns, numeric typing, row count, provenance banner). It never states that the call is side-effect free or read-only, and gives no hints on cost, caching, or error behaviour for a zero-argument introspection call.

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 sentences, both earning their place: the first front-loads the return contents, the second states the sequencing. It is tight, though the return enumeration is a slightly dense comma list where a leading "This returns..." would improve scannability.

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 zero parameters and no output schema, the description compensates by enumerating the returned fields, which is the right call for its complexity level. Only the read-only/no-side-effect confirmation and any sibling routing are missing.

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 no parameters, so there are no argument semantics to document and the baseline of 4 applies. The description correctly implies a trivial no-argument call rather than suggesting any filter or input.

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 (the Roofing Quotes UK dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and the provenance banner. It is clear but does not explicitly contrast itself with near neighbours like dataset_stats or dataset_provenance, which also surface dataset-level metadata.

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?

"Call this first to learn the schema" gives an explicit ordering instruction and a purpose, which is genuine when-to-use guidance. It stops short of naming an alternative or a when-not-to-use condition (e.g. use dataset_row for value-level detail), so it falls just below the top band.

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