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

With no annotations provided, the description carries the full burden, and it does well by enumerating the exact output contents: columns, numeric columns, row count, and provenance banner. This makes it clear the tool is a read-only metadata/schema inspection call, even though it does not explicitly state 'no side effects' or describe formatting.

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 deliver all essential information: what the tool returns and when to call it. There is no filler, and the usage instruction is front-loaded after the output summary.

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 zero-parameter introspection tool with no output schema, the description adequately covers the return values and the intended usage order. It could additionally mention whether the result is a list or a structured object, but this is a minor gap given the simplicity of the tool.

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 per the baseline this dimension deserves a 4. The description adds useful context about the fixed dataset ('Offdayly') and the kind of information returned, which is more than the empty schema alone provides.

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 identifies what the tool exposes: columns, numeric flags, row count, and provenance banner for the Offdayly dataset. It stops short of naming sibling tools or explicitly contrasting itself with dataset_provenance and dataset_stats, but the 'learn the schema' framing sets it apart as the schema-discovery entry point.

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 instruction 'Call this first to learn the schema' gives clear, actionable guidance on when to use this tool. It does not explicitly discuss when not to use it or name alternatives, but the sequencing advice is strong enough to route an agent correctly.

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