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

With no annotations, the description carries the behavioral disclosure burden. It discloses the output contents (columns, numeric indicators, row count, provenance banner) and implies a read-only metadata operation, but it does not explicitly state that calling it has no side effects or that it always reflects the full current schema.

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?

The description is brief and informative, with no wasted words. The main content is a single sentence, though the usage instruction is placed at the end rather than front-loaded.

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?

For a low-complexity tool with no parameters and no output schema, the description adequately explains what the agent will receive and why to call it. It might benefit from stating the return format, but the expected contents are enumerated well enough.

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 has zero parameters, so there are no parameter semantics to document. The baseline for a zero-parameter tool is 4, and the description appropriately focuses on what the tool returns instead.

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?

The description clearly states the tool exposes the dataset's columns, numeric flags, row count, and provenance banner, so an agent knows what it returns. It doesn't explicitly name a sibling to differentiate from, but the 'call this first to learn the schema' line gives it a distinct role among the sibling tools.

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?

The description gives clear usage context: 'Call this first to learn the schema.' This tells an agent when to invoke it, though it does not explicitly describe when to use other tools such as dataset_compare or dataset_search instead.

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