Summarize Dataset

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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world), and the description goes further by enumerating exactly which per-variable metrics are produced and how metrics vary by column type. That is genuine behavioral context beyond the annotations, though it omits practical traits such as cost or handling of very wide tables.

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?

The purpose and the 'entry point' claim are front-loaded in the first clause, followed by a structured enumeration of returned metrics. The per-type metric list is dense but each line carries information an agent needs; only slightly longer than necessary.

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 an output schema present, return values need not be explained, yet the description still documents them at a semantic level, and annotations plus full schema coverage handle safety and parameters. The only gap is guidance relative to the other read-oriented siblings, which a user of this complex a tool family would benefit from.

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?

Schema description coverage is 100%, so path, columns and apply_labels are already fully documented in the schema. The description adds no syntax, default or format detail for those parameters — the mention of value labels only implies what apply_labels touches. Baseline 3 is appropriate.

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 opens with a specific verb+resource (per-variable summary statistics) and states the tool's role explicitly as the preferred entry point for understanding a table. It also names the sibling it defers to (frequency) for labelled variables, so an agent can distinguish it from alternatives without opening any schema.

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?

It gives a clear usage condition — use this first to understand a table without writing SQL — and an explicit exclusion: when has_value_labels is true, use frequency to see decoded distributions. It does not, however, position itself against read_dataset, get_dataset_metadata or query_dataset, so the routing picture is only partial.

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