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KiHustle Type-Token Ratio Calculator
x402
KiHustle Type-Token Ratio Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-29).
Computes the type-token ratio (TTR) of a given text, a lexical diversity metric measuring unique word types relative to total tokens
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing the computed type-token ratio result and a success status indicator. The TTR value reflects the proportion of unique word types to total tokens in the submitted text, serving as a lexical diversity metric.
Example use cases
Essay vocabulary diversity check
“I just wrote this essay paragraph and want to know how varied my vocabulary is — can you compute the type-token ratio for it? Here it is: 'Education is the foundation of growth. Through learning, individuals grow, develop skills, and build better futures for themselves and society.'”
Comparing AI-generated vs human text
“I have two text samples — one written by a human and one by an AI — and I want to compare their lexical diversity. Can you calculate the type-token ratio for this AI-generated snippet: 'The system processes data efficiently. The data is then stored and the stored data is retrieved efficiently.'?”
Content quality scoring for NLP pipeline
“I'm running a content quality pipeline and need to score this blog excerpt for lexical richness using type-token ratio — please analyze this: 'Automation tools help creators save time, reduce repetitive tasks, and focus on creative work that drives real value.'”
Technical details
Endpoint
POSThttps://kihustle.tech/agents/api/v1/type-token-ratio?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a quick, low-cost lexical diversity metric for a single text sample. It is well-suited for NLP preprocessing pipelines, content quality scoring, plagiarism analysis workflows, or language learning applications where vocabulary richness needs to be quantified. Prefer it over full NLP suites when you only need the TTR statistic without sentiment, POS tagging, or other linguistic annotations.
Side effects
- Empty or missing text field returns an error or undefined result
- Extremely short texts (1-2 words) produce TTR of 1.0 which may be misleading
- Very long texts may produce artificially low TTR due to natural word repetition
- Non-text or heavily numeric input may yield unreliable diversity scores
- Malformed JSON request body returns a 400-level error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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