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

With no annotations, the description carries the full burden, and it usefully discloses the payload contents (columns, numeric flags, row count, provenance banner). However, it never states that this is a read-only/inspection operation, nor anything about caching, cost, or limits. Adequate but incomplete for an annotation-free 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 short sentences, with the return payload and the call-first instruction front-loaded and no filler. The opening fragment ("The columns, which of them are numeric...") is slightly elliptical but still efficient.

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 and no parameters, the description must describe the return value, and it does so by enumerating the returned fields. An agent has enough to call it and interpret the result; only the exact response shape/format is left unstated.

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 there is nothing to disambiguate and the baseline is 4. The description correctly adds no parameter commentary, as none is needed.

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 names the specific resources returned (columns, numeric flags, row count, provenance banner) for a named dataset, so an agent knows exactly what it gets. It is largely distinguishable from dataset_stats/dataset_top, though the included provenance banner overlaps with the sibling dataset_provenance tool, which blunts the differentiation slightly.

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 an explicit ordering/priority cue that routes the agent ahead of the other dataset_* tools. It does not state when NOT to use it or name a direct alternative, so it stops short of full 5-level guidance.

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