Assigns text to exactly one label from a given list using a local model. Useful for categorizing messages, routing content, or tagging inputs locally.
Classify text into exactly one of the given labels using a local model.
| Name | Required | Description | Default |
|---|---|---|---|
| hint | No | ||
| text | Yes | ||
| labels | Yes |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It does disclose two useful traits — processing is done by a 'local model' (no external API) and output is constrained to a single label — but says nothing about failure modes when text matches no label, determinism, or latency.
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?
A single tight sentence with the constraint front-loaded and no filler. It is appropriately sized, though the brevity is partly the source of the coverage gaps rather than pure economy.
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 annotations, no output schema, and 0% parameter description coverage, the description should carry much more. It omits what the return value looks like (label only, label plus score?), how 'hint' behaves, and any error behavior — real gaps for a 3-parameter tool.
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 0%, so the description must compensate. It clarifies that 'labels' is a candidate set from which exactly one is chosen, but the 'hint' parameter is never mentioned anywhere, leaving a required piece of the API undocumented.
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 (classify) and resource (text) plus the key constraint 'exactly one of the given labels'. The word 'text' implicitly separates it from the sibling classify_file, though it never names that alternative explicitly.
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?
No guidance on when to choose this over classify_file, extract, or summarize. The only routing signal is the implicit 'text' vs file distinction, which the agent must infer from sibling names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.