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

With no annotations provided, the description carries the full behavioral burden and does disclose the exact returned content and that it should be the first call. For a zero-argument, read-only introspection tool there is little else to disclose (no auth, mutation, or rate-limit concerns), though it never states it is read-only or side-effect free.

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 compact sentences with no filler, listing the payload items efficiently. The actionable instruction ("call this first") sits at the end rather than being front-loaded, which is a minor structural weakness.

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 values itself, and it does so by enumerating the four payload components. The only residual gap is the lack of differentiation from dataset_provenance, which mentions provenance as well.

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 no parameters, so there is nothing to document; the baseline for a zero-parameter tool is 4. The description appropriately spends its words on return content rather than nonexistent arguments.

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 a concrete return payload (columns, numeric flags, row count, provenance banner) for a specific named dataset, so the agent knows exactly what it gets. It does not explicitly contrast itself with the sibling dataset_provenance, whose name suggests overlapping content, leaving mild ambiguity about which to call for 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 an explicit ordering instruction relative to the other dataset_* tools, which is genuinely useful in a multi-step workflow. It stops short of naming which sibling to use for what after this call, so alternatives are left implicit.

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