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

No annotations are provided, so the description carries the burden, and it does disclose the return shape and the recommended call order. However it says nothing about safety profile, auth requirements, or whether the result is cached/static — though for a zero-argument read-only introspection tool the risk surface is minimal.

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 short sentences, zero redundancy, with the payload contents front-loaded and the calling instruction second. Every clause earns its place.

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 describe returns, and it does so well by enumerating the four things returned. Minor gap: it does not explain what the 'provenance banner' contains or its format, which an agent might want before deciding whether dataset_provenance is also needed.

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 the baseline is 4. There is nothing to disambiguate, and the schema is empty as expected.

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?

States a specific resource (the dataset's schema) and enumerates exactly what is returned: columns, which are numeric, row count, and provenance banner. This distinguishes it from siblings like dataset_row or dataset_stats, which return data values rather than schema metadata.

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 explicit ordering guidance that routes the agent correctly on entry. It does not name an alternative or exclusion, so it falls short of the 5-level when/when-not benchmark.

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