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Classify supplied items with Jev

classify_items
Read-onlyIdempotent

Assigns caller-supplied labels to each text item in bulk, adding 'other' and 'insufficient_context' labels when needed. Use for English classification tasks.

Instructions

Assign exactly one caller-supplied label to each item using Jev typed decisions. Adds other and insufficient_context labels. Use for English bulk classification, not prose generation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
labelsYes
instructionsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
usageYes
statusYes
classificationsYes
fallback_reasonNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already cover readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is well defined. The description adds that the tool automatically adds 'other' and 'insufficient_context' labels, which is a behavioral trait beyond the annotations. This is useful but minimal; it does not explain output format or edge cases, which is acceptable given the output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The core action is front-loaded, and the additional label behavior and usage constraints are stated efficiently. Every sentence contributes meaning without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity—nested item objects, a labels mapping, instructions, and an output schema—the description is too sparse. It does not explain how to populate the parameters correctly or what the output represents. While the output schema covers return values, the input semantics are largely unexplained, leaving an agent to infer from parameter names alone.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It only hints at 'items' and 'caller-supplied labels' but does not explain that labels is an object mapping label names to descriptions, that items are structured objects with id/text/metadata, or what instructions should contain. This is insufficient for a tool with nested objects and three required parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (assign labels to items), the resource (items), and the constraint of exactly one caller-supplied label per item. It also distinguishes itself from prose generation and from the sibling search_and_rank by being a classification tool. The phrase 'using Jev typed decisions' is specific enough to signal a distinct mechanism.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'Use for English bulk classification, not prose generation,' which gives positive and negative usage context. However, it does not mention the sibling tool search_and_rank or provide a when-not-to-use alternative beyond prose generation, so guidance is partial.

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

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