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 2026-10-13).

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

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

A JSON response containing ranked keywords from the input document with their computed TF-IDF scores relative to the provided corpus, indicating which terms are most distinctive and relevant in the document compared to the reference collection.

Example use cases

SEO keyword extraction for blog post

“I've written a new article about AI side hustles and I want to know which keywords stand out most compared to 15 similar articles I'll give you as a reference corpus — can you rank all the terms by their TF-IDF score?”

Content differentiation analysis

“Here are 20 product descriptions from my competitors, and here's my own product page — rank the keywords in my page by TF-IDF to show me which terms make my content distinctive from theirs.”

Academic document keyword extraction

“I have a research paper and a corpus of 30 related papers from the same field — run TF-IDF on my paper against that corpus and tell me which terms are most significant and unique to my document.”

Technical details

Endpoint

POSThttps://kihustle.tech/nexus/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 service without spinning up your own NLP infrastructure. It is especially useful for content creators, SEO analysts, and developers who need relative keyword importance scores for a single document against a known reference corpus, without requiring a full-featured NLP platform subscription.

Side effects

Empty corpus array returns error or meaningless scores
Document field missing or empty causes processing failure
Very large corpus or document may time out or exceed payload limits
Non-string array items in corpus may cause parsing errors
Generic response schema provides limited error detail beyond status field

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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