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

No annotations are supplied, so the description carries the full burden. It does imply a read-only introspection and discloses the returned content (columns, numeric flags, row count, provenance banner), which is useful since there is no output schema, but it says nothing about permissions, cost, or output format.

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 with no filler. The returned-content list is front-loaded, though the call-to-action ordering hint trails at the end rather than leading.

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 compensates by enumerating the four things returned, which is enough for an agent to know what it gets. It could say more about the shape of those values, but nothing essential is missing for a zero-argument introspection 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 takes zero parameters, so there is no parameter surface to document and the baseline is 4. The description correctly implies the call is argument-free and dataset-scoped.

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 names the exact resource (Immigration Adviser Finder dataset) and enumerates what the tool returns: columns, numeric flags, row count, and provenance banner. It is clearly a schema-introspection tool and reads differently from dataset_row or dataset_search, though it does not explicitly distinguish itself from the overlapping dataset_provenance and dataset_stats siblings.

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 clear sequencing guidance that tells the agent when in a workflow to reach for it. It stops short of naming alternatives or stating when not to use it, so it sits at 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.