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

No annotations are provided, so the description carries the burden. It discloses the return shape (columns, numeric-ness, row count, provenance banner), which implies a safe read of dataset metadata, but never states that it is read-only or that it makes no changes. Adequate but not rich behavioral context.

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, both front-loaded with the most important content (what it returns, then when to call it). Zero filler, though the phrase "which of them are numeric" is slightly awkward.

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 enumerate them concretely. For a zero-parameter schema-inspection tool this is complete enough to call correctly.

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, which sets the baseline at 4 per the rubric. There is nothing further for the description to clarify about 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?

Names the specific resource (the Wedding Cost Checker dataset) and enumerates exactly what it returns: columns, numeric flags, row count, and the provenance banner. It is distinguishable from dataset_stats or dataset_row by its schema-inspection role, though it does not explicitly name a sibling to contrast against.

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 that routes the agent to this tool before others. It stops short of naming conditions or alternatives (e.g., vs. dataset_provenance), so it is clear context without exclusions.

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