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

No annotations exist, so the description carries the full burden, and it does disclose the payload shape and the ordering directive. It does not explicitly say the call is side-effect free, that it takes no arguments, or how the provenance banner is formatted. For a zero-parameter read tool the risk is low, so gaps are minor but real.

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, with the return contents listed first and the usage directive second. The opening noun phrase is dense, but every clause earns its place 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?

With no output schema, the description must convey the return values, and it does so by naming all four pieces of returned content. It omits structural details such as whether columns come as a list of names or objects with types, leaving a modest gap for a schema-discovery tool.

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 schema has zero parameters, so there is nothing for the description to document; baseline 4 applies. The description correctly implies an argument-free call by not mentioning any inputs.

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?

It enumerates exactly what the tool returns (columns, numeric flags, row count, provenance banner) for a named dataset, which lets an agent distinguish it from dataset_stats or dataset_provenance. However, there is no explicit verb framing the operation as an introspection/read, and the provenance-banner content partially overlaps the dataset_provenance sibling.

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 directive relative to the sibling tools, which is exactly the context an agent needs. It stops short of stating when not to use it or what to call after, but the intended entry-point role is unambiguous.

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