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

With no annotations, the description carries the full burden, and it fully enumerates what the caller receives, which is the key behavioral fact for a no-parameter metadata reader. It omits anything about permissions or response format, but there are no destructive or side-effecting behaviors to disclose.

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, contents front-loaded followed by an actionable directive, with no filler.

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 is responsible for describing the return value, and it does so by enumerating the four things returned. It is complete enough to call correctly, though it does not mention ordering or format of the returned banner/column list.

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 the baseline is 4; there is no parameter syntax the description needs to compensate for.

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 outputs (columns, numeric flags, row count, provenance banner) of a specific dataset, so an agent knows exactly what this returns. It stops short of explicitly distinguishing itself from the sibling dataset_provenance, whose territory the 'provenance banner' overlaps.

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 clear ordering guidance for orientation before other dataset_* calls. It does not name alternatives or state when not to use it, but the intended workflow is unambiguous.

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