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x402 Academic Paper Search
x402
x402 Academic Paper Search is a paid API for AI agents from search.cyberwarex.com, paid per call via x402, $0.003/call, status unknown (last checked 2026-10-13).
Search for academic papers and research via a SearXNG-powered search endpoint, returning structured results with title, URL, authors, DOI, PDF link, snippet, and publication date.
Price / call$0.003
Success—
StatusUnknown
How this service works
Web search that returns structured SERP results as JSON. Runs on a dedicated SearXNG instance aggregating multiple engines, so there is no per-engine API key, quota or HTML scraping to manage. Each result carries title, URL, snippet, the source engine and a published date where available. Choose general web, news, or image results, up to 10 per call. Use it to ground an answer in current web sources, research a topic, or monitor news on a query.
Output
A JSON object containing the query string, a count of results, and an array of paper records each with: title, URL, DOI, PDF URL, authors list, journal name, source engine (e.g. arxiv), a text snippet, and published date (or null if unavailable).
Example use cases
Literature review on a research topic
“Find me up to 15 academic papers on transformer architectures for natural language processing — I need the titles, authors, DOIs, and PDF links to start a literature review.”
Locating a specific paper by title
“Can you find the paper 'Attention Is All You Need' and get me its DOI, authors, and a direct PDF link?”
Discovering recent studies for citation
“Search for recent research papers on CRISPR gene editing in cancer treatment — give me up to 10 results with snippets and publication dates so I can pick the most relevant ones to cite.”
Technical details
Endpoint
GEThttps://search.cyberwarex.com/papers?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need structured academic paper metadata (DOI, authors, PDF URL, publication date) aggregated across multiple search engines without managing individual API keys. It is ideal for literature reviews, citation lookup, or finding downloadable PDFs. Prefer this over general web search endpoints when the query is scholarly in nature and you need paper-specific fields like DOI, PDF URL, and author lists.
Side effects
- Missing required 'q' parameter returns a 400 validation error
- Query returns zero results if the topic is too obscure or misspelled
- max_results exceeds the allowed maximum of 20, causing a validation error
- Upstream search engines may be temporarily unavailable, returning partial or empty results
- Published date may be null for many results depending on the engine
- DOI and journal fields may be empty strings for preprints or non-indexed papers
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"count": 1,
"query": "graph neural networks drug discovery",
"results": [
{
"doi": "",
"url": "http://arxiv.org/abs/2108.01335",
"title": "Graph neural networks for drug discovery",
"engine": "arxiv",
"authors": [
"A. Smith",
"B. Lee"
],
"journal": "",
"pdf_url": "https://arxiv.org/pdf/2108.01335",
"snippet": "We review graph neural network architectures applied to...",
"published": "2021-08-03"
}
]
}
}Tags
x402
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