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

No annotations exist, so the description carries the full burden. It discloses the return contents (columns, numeric flags, row count, provenance banner), which is helpful, but never states that this is a read-only, side-effect-free introspection call or describes the response shape beyond a field list. Adequate but with gaps for a no-annotation tool.

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 contents front-loaded and the guiding imperative ("Call this first") closing it. Every clause earns its place 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?

With no output schema, the description enumerates the key return fields (columns, numeric flags, row count, provenance banner) so the agent knows what to expect. For a zero-parameter introspection tool this is nearly complete, though it omits any note on response size or structure.

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. The description correctly implies a parameterless, whole-dataset introspection call with nothing further to document.

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?

States a specific resource and enumerates exactly what is returned: columns, numeric flags, row count, and provenance banner. It is clearly a schema-introspection tool rather than a data-fetching one, though it does not explicitly differentiate itself from overlapping siblings like dataset_stats or dataset_provenance.

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 a clear sequencing instruction that tells the agent when to reach for this tool ahead of the others. It stops short of naming when-not to use it or pointing to specific alternatives, but the ordering guidance is explicit and actionable.

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