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ONE Engine Text Token Mentions Extractor (Top 10)
x402
ONE Engine Text Token Mentions Extractor (Top 10) is a paid API for AI agents from one-search.one-engine.workers.dev, paid per call via x402, $0.03/call, status unknown (last checked 2026-10-02).
Extracts the top 10 most frequently mentioned tokens or terms from a block of text up to 50,000 characters.
Price / call$0.03
Success—
StatusUnknown
How this service works
Low-cost pay-per-call utility APIs for autonomous agents using x402 on Base.
Output
Returns a JSON object containing the top 10 most frequently mentioned tokens from the submitted text, likely including each token and its mention count or rank, enabling downstream frequency analysis or topic summarization.
Example use cases
Trending topics in news article
“Take this long news article and tell me the top 10 words or phrases that appear most frequently — I want to know what the piece is really focused on.”
Brand mention analysis in reviews
“Here's a batch of customer reviews concatenated together — find the top 10 most mentioned tokens so I can see which product features or complaints come up the most.”
Research paper keyword extraction
“I have this 40-page research paper as text — extract the top 10 most frequently mentioned terms so I can quickly understand its core subject matter.”
Technical details
Endpoint
POSThttps://one-search.one-engine.workers.dev/v3/text-token/mentions/t10?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call solution to rank the most frequently occurring tokens in a text body without running a full NLP pipeline. It is ideal for autonomous agents needing quick frequency-based term extraction at low cost ($0.03 per call) on Base via x402, especially when you don't need semantic clustering or entity recognition — just raw mention counts.
Side effects
- Text exceeding 50,000 characters may be rejected or truncated
- Empty or whitespace-only text may return empty results or an error
- Payment failure via x402 on Base will block the request
- Ambiguous tokenization boundaries may affect token count accuracy
- Non-English or mixed-language text may produce unexpected token segmentation
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| textrequired | string | Text up to 50000 characters |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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