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

With no annotations, the description carries the full burden but does disclose the payload contents an agent receives (column list, numeric flags, row count, provenance banner), so the return shape is inferable. It never states the operation is a read-only metadata query, which is an implicit but unstated guarantee.

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 sentences, no filler, and the action cue ("call this first") is placed after the payload summary so the agent knows both what it gets and when. Nothing is wasted.

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, no-output-schema tool, listing the returned fields is enough for an agent to know it is safe and useful as an entry point. The only missing element is an explicit statement that this is a read-only, side-effect-free call.

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?

Zero parameters, so the baseline is 4; there is nothing to disambiguate. The description correctly avoids inventing parameters that do not exist in the empty schema.

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 concrete resources returned: columns, which are numeric, row count, and the provenance banner, scoped to the Lettza dataset. It is distinguishable from siblings like dataset_row and dataset_search, though the overlap with dataset_provenance and dataset_stats (both banner/row-count adjacent) is not called out.

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, which is a clear when-to-use signal. It stops short of naming alternatives or stating when another sibling (e.g. dataset_stats) should be preferred instead.

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