SiteCheck Keyword Extraction

x402

SiteCheck Keyword Extraction is a paid API for AI agents from api.sitecheck-api.workers.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-16).

Extracts ranked keywords from text using the RAKE algorithm, returning scored keyword phrases with frequency counts

Price / call$0.001

Seller volume →—

Success—

StatusUnknown

How this service works

Keywords and keyphrases from a text by RAKE (Rose et al., 2010) in pure code: phrases between stopwords and punctuation, scored by word degree and frequency, with counts. Stopword lists for en, de, fr, es, it, pt, nl and tr. Body { text, language, limit }: up to 100 KB, limit 1 to 50.

Output

Returns a JSON object containing a list of keyword phrases, each with a relevance score (computed via RAKE algorithm) and occurrence count, along with the total keyword count, the detected or specified language, and the timestamp of the analysis.

Example use cases

SEO keyword audit for blog post

“Pull out the top 20 keywords from this blog post draft so I can check if it's targeting the right search terms — here's the text: 'Cloud computing has revolutionized how businesses store and process data, enabling scalable infrastructure on demand...'”

Topic tagging for German content

“Extract the top 10 keywords from this German product description so I can auto-tag it in our CMS: 'Dieses Smartphone bietet eine hochauflösende Kamera, langen Akku und schnelle Prozessorleistung für anspruchsvolle Nutzer.'”

Key phrase mining from customer feedback

“Analyze this customer review and give me the 5 most important keyword phrases with their scores: 'The delivery was extremely fast but the packaging was damaged and the product instructions were completely missing.'”

Technical details

Endpoint

POSThttps://api.sitecheck-api.workers.dev/api/text/keywords?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need lightweight, fast, pay-per-call keyword extraction without setting up an NLP pipeline. It is ideal for agents processing documents in supported European languages (en, de, fr, es, it, pt, nl, tr) that need scored keyword phrases rather than simple word frequencies. Prefer it over embeddings-based approaches when you need human-readable ranked terms rather than vector representations.

Side effects

Text exceeding 100 KB limit may be rejected or truncated
Unsupported language codes return an error or fall back to English
Empty or whitespace-only text returns zero keywords
Limit values outside 1–50 range may be clamped or cause validation errors
Very short texts may yield no meaningful keyword phrases

Input

Schema · Request body

Field	Type	Description
text	string	The text, up to 100 KB (JSON body field)
limit	string	Keywords to return, 1 to 50, default 10
language	string	en (default), de, fr, es, it, pt, nl or tr

Output

Schema · Response

{
  "type": "json",
  "example": {
    "count": 4,
    "source": "Computed here (RAKE, Rose et al. 2010), stopword list: short original lists per language",
    "keywords": [
      {
        "count": 1,
        "score": 6.5,
        "keyword": "machine learning"
      },
      {
        "count": 1,
        "score": 5,
        "keyword": "deep learning"
      },
      {
        "count": 1,
        "score": 1,
        "keyword": "data"
      }
    ],
    "language": "en",
    "checkedAt": "2026-10-15T13:48:02.045Z"
  }
}

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