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

Annotations flag this as non-read-only and destructive; the description explains the source of that risk ('a group left out is dropped', same-named groups merged) rather than merely restating it. It further adds traits absent from the annotations: no re-read and no charge on the naming pass, and an explicit prompt-injection warning that row text/labels/file names must not be treated as instructions. This is meaningful disclosure beyond the structured fields.

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?

Three sentences with no filler, and the core purpose plus return contents are front-loaded before the second-call workflow. It is dense and slightly packed with parenthetical rules, but every clause carries information an agent needs.

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 correctly carries the burden by enumerating what comes back (group size, distinguishing words, sample rows). It also covers the two-call lifecycle, cost behavior, and the data-safety constraint, leaving nothing an agent needs in order to invoke it correctly.

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 the baseline is 3, and the schema itself already documents `groups`, `split` and `names`. The description nevertheless adds real semantics for `names` (each entry becomes a category, duplicates merge, omissions are dropped) that the schema only gestures at with 'the groups to keep', so it earns slightly above baseline.

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 verb+resource ('Find the categories in a cold start's rows by grouping similar ones') and even describes the return payload (group size, distinguishing words, example rows). It is very clear what the tool produces, but it never names or contrasts sibling tools such as classify or suggest_labels, which occupy adjacent conceptual space, so the sibling-differentiation condition for a 5 is unmet.

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?

Gives concrete workflow guidance: run it first to discover groups, then 'call again with names' to promote named groups to categories, including the merge/drop rules of that second pass. It does not, however, state when to prefer it over alternatives like classify or suggest_labels, so usage context is clear but without explicit exclusions.

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