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

No annotations exist, so the description carries the full behavioral burden. It discloses the return payload (columns, numeric flags, row count, provenance banner), which is genuinely useful, but says nothing about whether the dataset is static or refreshed, or any limits on the call. Adequate but incomplete 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?

One short sentence plus a one-clause imperative. Nothing is repeated and the actionable instruction (call first) lands immediately after the content description.

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 annotations, the description must convey the return shape, and it does so by enumerating the four returned elements. A note on what the 'provenance banner' contains, or how numeric flags are represented, would close the remaining gap.

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 are no parameter semantics to document; the baseline of 4 applies. The description correctly describes an input-free call rather than implying optional 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?

Names the specific resource (Procedure Cost Checker dataset) and enumerates exactly what it returns: column names, numeric flags, row count, and the provenance banner. That distinguishes it reasonably from dataset_stats and dataset_provenance, though the overlap with those siblings is not called out.

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, which is real usage guidance for an agent exploring an unfamiliar dataset. It does not, however, say when NOT to use it or how it differs from dataset_stats/dataset_provenance.

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