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

With no annotations provided, the description carries the full burden of behavioral disclosure. It tells the agent exactly what information will be returned: columns, numeric-ness, row count, and provenance banner. It does not explicitly state read-only behavior or error conditions, but for a no-parameter metadata tool the disclosed outputs are sufficient.

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 tight sentences with no filler. The core deliverable list is front-loaded, and the usage instruction 'Call this first' is placed at the end as a purposeful directive.

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 tool with no output schema, the description is nearly complete: it names the dataset, lists the outputs, and indicates when to call it. It could further explain how the result feeds into sibling tools, but that is not essential for correct invocation.

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 the schema already covers everything. The description correctly avoids adding parameter chatter, and the baseline of 4 applies because there is nothing for the description to clarify.

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 what the tool returns: columns, numeric flags, row count, and provenance banner for the Corp Tax Calculator dataset. It also says 'Call this first to learn the schema,' which positions its role in the workflow. However, it doesn't explicitly differentiate itself from siblings like dataset_provenance or dataset_stats, which might overlap on provenance or row count.

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 explicit guidance to call this tool first when learning the schema. It does not mention when to avoid it or which alternatives to use, but for a zero-parameter schema-introspection tool this is reasonably clear context.

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