Decide which option fits your text by classifying it against your criteria, returning confidence scores and relative probabilities for each choice.
Fast, calibrated multi-option classification with confidence and relative probabilities.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Context or text to classify | |
| criteria | Yes | Mapping of option key to description | |
| question | Yes | Classification instruction or question |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It adds useful behavioral context by promising calibrated output and relative probabilities rather than just a label. However, it does not disclose the exact return shape, side effects, or behavior for ambiguous inputs.
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 short, front-loaded sentence with no filler. It is concise, but slightly under-specified for a tool with no annotations and no output schema, so it does not quite earn a 5.
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?
The description omits when to use the tool, what the question field should contain, and how the returned confidence/probabilities are structured. With no output schema and no annotations, the one-sentence description is not sufficient for reliable selection and invocation.
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?
All three parameters are already described in the schema (100% coverage), so the baseline is 3. The description connects criteria to the multi-option classification concept but adds no parameter-level detail beyond what the schema already provides.
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 identifies the core operation as 'multi-option classification' and names key outputs ('confidence and relative probabilities'), which is a clear verb+resource statement. It does not explicitly differentiate this from sibling decision tools such as jev_decide_noul or jev_decide_score, so it stops short of 5.
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 is given for when to use this tool instead of alternatives like jev_decide_noul or jev_decide_score. The word 'Fast' hints at a possible use case, but it is not actionable enough to help an agent choose between sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.