classify

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

Goes well past the readOnly/openWorld annotations: it discloses the privacy model (free, private, offline, on-device), hard input bounds (~3,500 words, 2-50 labels), an output guarantee (the answer is always one of your labels, or several with `multi`), and an accuracy caveat with mitigation ('subtle or ambiguous text can be mislabelled, so spot-check'). That is unusually rich behavioral disclosure for a pure-inference tool.

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?

Four sentences, each doing distinct work: purpose first, then use cases, then limits, then exclusions. No filler or repetition, and the most decision-relevant information (what it does and where it applies) is front-loaded.

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?

Full picture for a five-parameter inference tool with no output schema: input constraints, output constraint, multi-label behavior, accuracy risk, privacy posture and scope exclusions are all present. Nothing an agent needs to call this correctly is missing.

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 text, path, multi, labels and instructions are all already documented. The description mostly restates those fields (3,500 words, 2-50 labels, text-or-path) and only marginally adds the framing that the answer is drawn from the label set. Baseline 3 is correct when the schema carries the parameter burden.

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 and resource: pick the best label for a text from a caller-supplied list, running on the on-device Apple Foundation Models. The scope exclusion ('Not for code, maths or reasoning') sharpens the boundary, but it never names the sibling tools (extract, ocr, summarise), so routing among them relies on inference.

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?

Concrete use cases are given ('triage: sorting tickets, emails, comments or log lines into categories') plus an explicit when-not ('Not for code, maths or reasoning'). It lacks any named alternative tool or a rule for choosing between this and summarise, so it falls short of full when/when-not/alternatives coverage.

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