KiHustle TF-IDF Keyword Ranker

x402

KiHustle TF-IDF Keyword Ranker is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).

Ranks keywords in a document by their TF-IDF scores relative to a corpus of reference documents

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

Returns a JSON object with ranked keywords from the input document, scored by their TF-IDF weight relative to the provided corpus, along with a success status indicator.

Example use cases

SEO content differentiation analysis

“I have a draft article about AI side hustles and 15 competitor articles. Can you run TF-IDF keyword ranking on my draft against those competitor texts so I can see which keywords make my content unique?”

Blog post keyword prioritization

“Rank the keywords in this product review I wrote by their TF-IDF scores, using my last 20 published posts as the corpus — I want to know which terms are most distinctive to this review.”

Research document term extraction

“I have a research summary and a corpus of 30 related academic abstracts. Use TF-IDF to tell me which terms in my summary are the most statistically significant compared to the rest of the corpus.”

Technical details

Endpoint

POSThttps://kihustle.tech/agents/api/v1/tf-idf-keyword-ranker?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a lightweight, pay-per-call TF-IDF keyword ranking computation without setting up your own NLP infrastructure. It is well-suited for content creators, SEO analysts, or AI agents that need to identify statistically distinctive terms in a document relative to a reference corpus on demand.

Side effects

Empty corpus array returns an error or meaningless scores
Document field missing or empty causes processing failure
Very large corpus or document may time out or return partial results
Malformed JSON in request body causes 400-level error
Non-string items in corpus array may cause unexpected behavior

Input

Schema · Request body

Field	Type	Description
corpus	array
document	string

Output

Schema · Response

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

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