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

No annotations and no output schema, so the description carries the full burden; it partly discharges that by enumerating the returned fields. It does not state that the operation is read-only/side-effect free, nor any cost or caching behavior, so the safety profile is left to inference.

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, no filler: the payload is front-loaded and the call-order directive follows. Every clause adds information (fields returned plus sequencing) and nothing is repeated.

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 read tool with no output schema, the description covers the essential return surface and the call ordering, which is sufficient to invoke it. It could be slightly richer by clarifying how its provenance output differs from the dedicated dataset_provenance sibling.

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?

Zero parameters, which is the baseline-4 case by rule. The description correctly implies the tool is argument-free via the directive to call it first, with nothing to misconfigure.

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 states concretely what the tool returns: columns, which are numeric, the row count, and the provenance banner, scoped to a named dataset. That is enough for an agent to distinguish it from dataset_stats or dataset_row, though the 'provenance banner' element overlaps with the sibling dataset_provenance and the overlap is not disambiguated.

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 sequencing guidance, which is exactly the routing information an agent needs. It stops short of naming alternatives (e.g., when to prefer dataset_provenance or dataset_stats instead), so no exclusions are stated.

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