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by app.agentbit
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AI Classify (zero-shot)

agentbit.ai_classify
Read-onlyIdempotent

Classify text into your labels with confidence and per-label scores. Single- or multi-label. Cheap and high-volume: routing, tagging, moderation triage, intent. Pay-per-call via x402 (USDC, Base).

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>",
      "labels": "<labels>"
    },
    "name": "agentbit.ai_classify"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesThe text to classify (required).
labelsYesAllowed labels (required, up to 30).
_paymentNoOptional base64 x402 PAYMENT-SIGNATURE payload. Omit to receive payment requirements.
multi_labelNoAllow multiple labels (default false).
instructionsNoOptional guidance on how to classify.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context beyond annotations: pay-per-call via x402 (USDC, Base) and that output includes confidence and per-label scores. No contradiction with annotations.

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?

Three sentences with no filler. The primary action is front-loaded, followed by mode, use cases, and payment context. Every sentence earns its place.

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?

With no output schema, the description compensates by stating the output nature (confidence and per-label scores). It also covers payment behavior and use cases. It does not specify the exact response format, but for a simple classification tool, the combination of description and schema is sufficient.

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 100%, so the schema already documents all parameters. The description adds minor semantic value by mentioning 'Single- or multi-label' (clarifying multi_label) and 'your labels' (clarifying labels), but it does not provide meaningful details beyond the schema.

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 states a specific verb and resource: 'Classify text into your labels with confidence and per-label scores.' It clearly distinguishes this from sibling AI tools like ai_summarize, ai_translate, and ai_extract by emphasizing classification into user-defined labels, single/multi-label modes, and concrete use cases like routing and tagging.

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?

The description gives clear context for when to use the tool: 'Cheap and high-volume: routing, tagging, moderation triage, intent.' It does not explicitly name alternatives or state when not to use it, but the use-case list is enough to guide an agent toward this tool over siblings.

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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