Lore

Official

by agentkitai

Overview Schema Source Related Servers Discussions Admin

Python

Remote

classify

Classify any text by intent, domain, and emotion to understand its nature before storing, without retaining the data.

Instructions

Classify a piece of text by intent, domain, and emotion. Returns structured classification without storing anything. USE THIS WHEN: you want to understand the nature of a piece of text before storing it, or to analyze conversation patterns.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "classify"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jun 24, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses that nothing is stored and returns structured classification, indicating non-destructive behavior and output structure.

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?

Two concise sentences plus a clear usage cue. Every sentence adds value, front-loaded with the main action.

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

Completeness4/5

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

For a simple single-parameter tool with an output schema, the description covers purpose, non-storage, and use cases. No annotations needed extra context; it's nearly complete.

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

Parameters3/5

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

Schema description coverage is 0%, so description must compensate. It implicitly explains the 'text' parameter by saying 'classify a piece of text', but adds no format or constraints. Slightly above baseline due to mentioning classification dimensions.

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 tool classifies text by intent, domain, and emotion, and returns structured classification without storing. This distinguishes it from siblings that store or modify data.

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?

Explicitly states use cases: before storing text or analyzing conversation patterns. Lacks explicit when-not-to-use but provides clear positive guidance.

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