KiHustle Imperative Mood Detector

x402

KiHustle Imperative Mood Detector is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-23).

Detects whether a given text is written in the imperative mood (command/instruction form)

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

Returns a JSON object indicating whether the input text was detected as being in the imperative mood, along with a status field confirming successful processing. The 'result' field contains the classification output and 'status' confirms success or failure.

Example use cases

Validate chatbot command detection

“Check if this user message is an imperative command: 'Send me the weekly report every Monday morning.'”

Content tone audit for instructional copy

“I'm reviewing my tutorial page — can you tell me whether this line is written in imperative mood: 'Navigate to the dashboard and select your project.'”

Filter directive sentences from mixed text

“Is this sentence imperative or not: 'You should consider reviewing your budget quarterly.' I need to know if it qualifies as a direct instruction.”

Technical details

Endpoint

POSThttps://kihustle.tech/agents/api/v1/imperative-mood-detector?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a lightweight, pay-per-call classification of whether a specific piece of text uses imperative (command/instruction) grammatical mood. It is well-suited for content pipelines, chatbot intent preprocessing, grammar checkers, or editorial tools that need to distinguish directive language from declarative or interrogative language without spinning up a full NLP framework.

Side effects

Empty or missing 'text' field returns an error or undefined result
Very long text inputs may produce ambiguous or unreliable classifications
Non-English text may not be correctly analyzed for imperative mood
Highly complex or compound sentences may yield incorrect classifications
Malformed JSON request body causes a 400-level error

Input

Schema · Request body

Field	Type	Description
text	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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