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AllTheBot Keyword Extraction & NER
x402
AllTheBot Keyword Extraction & NER is a paid API for AI agents from api.zkrobotics.fr, paid per call via x402, $0.007/call, status unknown (last checked 2026-10-09).
Extracts ranked keywords and named entities (people, organizations, locations, dates, money, products) from any text in any language, returning clean JSON.
Price / call$0.007
Success—
StatusUnknown
How this service works
Pay-per-call APIs for AI agents over x402: token safety reports, Base and Ethereum on-chain reads, crypto prices, domain checks (DNS, WHOIS, SSL, email), finance data (FX, SEC), worldwide weather forecasts, France alerts, text AI, fun content. USDC on Base, no account, no API key.
Output
A JSON object containing two fields: 'keywords' (an array of ranked keyword strings extracted from the text) and 'entities' (an array of objects each with 'text' and 'type' fields, where type is one of: person, organization, location, date, money, or product).
Example use cases
Entity tagging for news article
“Here's a news snippet: 'Apple CEO Tim Cook announced a $500 million investment in Austin, Texas on January 15, 2025.' Can you extract the keywords and identify all named entities — people, organizations, locations, dates, and money amounts?”
Contract entity extraction
“I need to pull out all the important entities from this contract clause: 'Google LLC agrees to pay Microsoft Corporation $2 million by December 31, 2025 for services rendered in Seattle.' Get me the keywords and classify each entity.”
Multilingual NLP for French text
“Run keyword extraction and named entity recognition on this French text: 'Emmanuel Macron a rencontré des représentants d'Airbus à Toulouse le 10 mars 2024 pour discuter d'un contrat de 3 milliards d'euros.' What are the keywords and which entities are people, organizations, locations, dates, or money?”
Technical details
Endpoint
GEThttps://api.zkrobotics.fr/x402/entities?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both keyword extraction AND named entity recognition in a single call for any language, without deploying your own NLP model. It is particularly useful for multilingual text where you need clean JSON output with typed entities (people, orgs, locations, dates, money, products) and ranked keywords in one pass. Prefer it over general-purpose LLM prompting when you want consistent, structured entity tagging at low cost ($0.007/call) without prompt engineering overhead.
Side effects
- Text exceeding 6000 characters returns a validation error
- Empty or missing 'text' query parameter returns a 400 error
- Ambiguous text with no clear entities may return empty entity arrays
- Non-UTF8 encoded text may fail to parse correctly
- Very short or nonsensical input may return minimal or empty keywords
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"entities": [
{
"text": "Airbus",
"type": "organization"
},
{
"text": "Toulouse",
"type": "location"
},
{
"text": "March 3, 2026",
"type": "date"
},
{
"text": "1.5 billion euros",
"type": "money"
}
],
"keywords": [
"Airbus",
"aircraft order",
"Toulouse"
]
}
}
}Tags
x402
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