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

No annotations are provided, so the description carries the full burden, and it does disclose the read-only nature of the operation by describing its inspection output (columns, row count, banner). It omits any statement about permissions or whether the banner reflects live configuration, but for a zero-parameter metadata read that is a minor gap.

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 with the returned content front-loaded and the imperative usage hint placed last, which is the right ordering for an entry-point tool. Slightly compressed phrasing ("the columns, which of them are numeric...") but nothing wasted.

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 no output schema, the description must convey return values, and it lists the four outputs an agent needs before calling the siblings. No safety caveat is required for a parameterless read, so the definition is essentially complete, with only the numeric-flag format left unspecified.

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 is nothing for the description to clarify; baseline 4 applies. No parameter-level detail is expected or missing here.

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 concrete resource (the Stocktaka dataset) and enumerates exactly what it returns: column names, which are numeric, the row count, and the provenance banner. That is far more specific than the title, though it never explicitly contrasts itself with siblings like dataset_provenance or 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?

"Call this first to learn the schema" gives explicit ordering guidance and implies the tool should precede the other dataset_* calls. It stops short of naming a non-overlapping alternative or stating exclusions, so it is clear context without full routing.

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