Selects one label from a predefined set and returns its probability distribution for accurate classification.
Choose one label from a closed set and return its probability distribution.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | ||
| state | Yes | ||
| labels | Yes | ||
| instructions | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | ||
| model | Yes | ||
| usage | Yes | ||
| answer | Yes | ||
| evaluation | Yes |
Changes observed during successful MCP inspections.
v0.6.0Input schema / properties / labels / maxPropertiesAdded value: +255Input schema / properties / labels / minPropertiesAdded value: +1v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive), so the bar for additional disclosure is lower. The description adds useful behavioral context by mentioning the closed set and probability distribution output, but doesn't address edge cases like ambiguous labels or handling of unexpected 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 compact sentence with no filler, front-loading the action and output. It is easy to parse, though the brevity sacrifices parameter-level detail.
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?
For a tool with nested objects, four parameters, zero schema description coverage, and eight siblings, one sentence is insufficient. The output schema may document return values, but the description lacks input semantics, usage context, and guidance on when this tool is the right choice.
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 0%, so the description needed to compensate by explaining state, instructions, labels, and model. It only hints at the labels parameter via 'closed set' and leaves the other parameters essentially unexplained, forcing the agent to rely on names alone.
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 states a specific operation: choose one label from a closed set and return its probability distribution. This clearly conveys a classification behavior and distinguishes it from generic check/verify tools, though it doesn't explicitly name a sibling alternative.
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?
There is no guidance on when to use classify versus evaluate, score, verify, or other siblings. The phrasing implies a classification task, but no exclusions, alternatives, or selection criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.