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QuickBuild Web Summarizer
x402
QuickBuild Web Summarizer is a paid API for AI agents from quickbuild.quynguyenbac.workers.dev, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-21).
Fetches a public URL or accepts raw text and returns an AI-generated summary with key points, topics, entities, sentiment, and facts
Price / call$0.02
Success—
StatusUnknown
How this service works
Landing pages ($15), Google Sheets/Apps Script automation ($10), scrapers, scripts & Telegram bots (from $15), broken-link + sitemap audit reports ($10). See a working demo first, pay after.
Output
Returns a JSON object containing: a plain-text summary (1-8 sentences depending on length setting), an array of key points, detected topics, named entities with types, sentiment label, page title, intended audience, structured facts, ISO 639-1 language code, content type, whether the content was truncated, the number of characters analyzed, and the AI model used.
Example use cases
Research article quick digest
“Summarize https://arxiv.org/abs/2310.06825 for me — medium length, up to 5 key points, focused on 'methodology', and give it to me in English.”
Competitor pricing page analysis
“Pull up https://stripe.com/pricing and give me a short summary focused on 'pricing', with 3 key points — I want to know their fee structure at a glance.”
Raw text briefing from a document
“Here's a long block of notes I pasted — can you summarize it for me in 5-8 sentences with up to 8 key points? The text is: 'Q3 planning session transcript: We discussed expanding to APAC markets by Q1 next year, hiring 10 engineers, and cutting infrastructure costs by 20%...'”
Technical details
Endpoint
GEThttps://quickbuild.quynguyenbac.workers.dev/api/summarize?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, structured AI summary of any public webpage or raw text, especially when you also need extracted entities, sentiment, topics, and key facts in a single call. It is ideal for content pipelines, research assistants, competitive intelligence, and digest generation. Prefer it over generic LLM calls when you want standardized output fields (keyPoints, entities, sentiment) without prompt engineering.
Side effects
- Private or IP-based URLs are refused with an error
- Text exceeding 200,000 characters will be rejected
- URLs that are inaccessible or return non-200 status codes may fail
- Focus parameter exceeding 200 characters will be rejected
- Unsupported ISO 639-1 language codes may produce unexpected output
- Rate limiting or payment failures result in 402 responses
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"url": "https://example.com/",
"facts": [],
"model": "@cf/meta/llama-3.3-70b-instruct-fp8-fast",
"title": "Example Domain",
"source": "url",
"topics": [
"domains",
"documentation"
],
"summary": "Example Domain is a reserved domain for use in documentation examples without prior coordination.",
"audience": "developers and writers",
"entities": [
{
"name": "IANA",
"type": "organization"
}
],
"language": "en",
"keyPoints": [
"Reserved for illustrative examples",
"Usable in documents without permission"
],
"sentiment": "neutral",
"truncated": false,
"contentType": "other",
"charsAnalyzed": 180
}
}Tags
x402
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