Classify text with the local model

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

No annotations are provided, so the description carries the full burden — and it delivers richly. It discloses that output is validated against label set, returns matched=false with raw reply on hedging, can return declined=true, and honestly warns that a small model will still pick a confident in-set label for out-of-set text ('triage, not a verdict'). This is unusually candid behavioral disclosure beyond what any structured field could convey.

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 description is a single focused paragraph that front-loads the core behavior then layers in caveats and use cases. Every sentence earns its place, but it's slightly dense and could be split into short sections for scannability. Efficient and non-redundant despite its length.

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?

This is a moderately complex tool (5 params, nuanced behavior around declined/matched and model error modes). With 100% schema coverage, no output schema, and no annotations, the description fully compensates: it explains validation behavior, edge cases (hedging, no-label), trust limitation, and appropriate use cases. The behavioral nuance of a model-based classifier is well covered.

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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the behavior behind allowNone (the declined escape hatch, forced-choice mislabelling risk) and clarifying that labels are the constraint the reply is validated against. It doesn't add detail on model or instruction params beyond the schema, but the schema already covers them. Marginal added value justifies a 4.

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 uses a specific verb+resource pair ('classify text into labels you supply') and clearly distinguishes from siblings local_ask and local_models by framing it as triage/classification rather than open-ended chat. It even names concrete uses (log lines, error vs warning, which files look relevant), grounding the purpose.

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?

The description explicitly tells when to use it ('Good for triage'), frames its limits ('triage, not a verdict'), and gives concrete when-to-use examples. It advises on the allowNone flag ('Set allowNone=false only when a forced choice is genuinely wanted'). It's missing an explicit 'not for X, use sibling instead' but the triage framing plus sibling names imply exclusion well.

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