meld402 Academic Paper Search

x402

meld402 Academic Paper Search is a paid API for AI agents from meld402.vercel.app, paid per call via x402, $0.15/call, status unknown (last checked 2026-09-14).

Searches academic literature by query and optional year range, returning ranked papers with titles, authors, citations, and snippets aggregated via SerpApi — paid per call over x402.

Price / call$0.15

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How this service works

14 paid data endpoints for autonomous agents and developers. Covers crypto intelligence (token, chain, Solana, gas), markets (FX, market pulse), location (weather), and research (news, brief). Each route aggregates multiple public sources into one stable schema with a single USDC payment over x402 on Base.

Output

Returns a JSON object with a list of ranked papers (up to 20), each containing title, authors array, publication year, citation count, abstract snippet, source URL, and position. Also includes total_results count, query echo, data sources used (SerpApi), timestamp, and attribution string.

Example use cases

Literature review for AI research

“Find me the 15 most relevant academic papers on large language model alignment published between 2020 and 2024 — I need titles, authors, and citation counts for a literature review.”

Tracking recent climate science

“Search for the 10 most recent academic papers on ocean acidification published between 2022 and 2025, and show me the snippets and URLs so I can read the most relevant ones.”

Citation hunting for a thesis

“Look up up to 20 academic papers about CRISPR off-target effects, covering any year, and tell me which ones have the most citations.”

Technical details

Endpoint

POSThttps://meld402.vercel.app/api/web/academic

When to prefer

Choose this endpoint when an AI agent needs structured, citation-rich academic paper metadata from a stable schema without managing individual SerpApi credentials. Ideal for literature review automation, research assistant workflows, or any agent task requiring ranked scholarly results with citation counts. Prefer over raw SerpApi calls when a single USDC micropayment per query (via x402 on Base) is acceptable and the agent needs a normalized, multi-source-ready response format.

Side effects

Empty query string returns validation error
Query exceeds 500 characters returns schema validation error
Insufficient USDC balance / failed x402 payment returns 402 Payment Required
SerpApi upstream unavailable causes degraded or empty results with source listed in degraded array
num outside 1-20 range returns schema validation error
year_from greater than year_to may return zero results or unexpected ordering

Input

Schema · Request body

Field	Type	Description
num	integer	Number of papers
query	string	Academic search query
year_to	integer	Latest publication year
year_from	integer	Earliest publication year

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "query": "transformer architecture attention mechanism",
      "papers": [
        {
          "url": "https://arxiv.org/abs/1706.03762",
          "year": 2017,
          "title": "Attention Is All You Need",
          "authors": [
            "Vaswani",
            "Shazeer",
            "Parmar"
          ],
          "snippet": "We propose a new simple network architecture, the Transformer...",
          "position": 1,
          "citations": 125000
        },
        {
          "url": "https://arxiv.org/abs/1810.04805",
          "year": 2019,
          "title": "BERT: Pre-training of Deep Bidirectional Transformers",
          "authors": [
            "Devlin",
            "Chang",
            "Lee"
          ],
          "snippet": "We introduce BERT, a method for pre-training deep bidirectional...",
          "position": 2,
          "citations": 85000
        }
      ],
      "total_results": 45000
    },
    "meta": {
      "as_of": "2026-08-24T00:00:00.000Z",
      "sources": [
        "serpapi"
      ],
      "degraded": [],
      "attribution": [
        "Search data by SerpApi (serpapi.com)"
      ]
    }
  }
}

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How this service works

Output

Example use cases

Literature review for AI research

“Find me the 15 most relevant academic papers on large language model alignment published between 2020 and 2024 — I need titles, authors, and citation counts for a literature review.”

Tracking recent climate science

“Search for the 10 most recent academic papers on ocean acidification published between 2022 and 2025, and show me the snippets and URLs so I can read the most relevant ones.”

Citation hunting for a thesis

“Look up up to 20 academic papers about CRISPR off-target effects, covering any year, and tell me which ones have the most citations.”

Technical details

Endpoint

POSThttps://meld402.vercel.app/api/web/academic

When to prefer

Side effects

Empty query string returns validation error
Query exceeds 500 characters returns schema validation error
Insufficient USDC balance / failed x402 payment returns 402 Payment Required
SerpApi upstream unavailable causes degraded or empty results with source listed in degraded array
num outside 1-20 range returns schema validation error
year_from greater than year_to may return zero results or unexpected ordering

Input

Schema · Request body

Field	Type	Description
num	integer	Number of papers
query	string	Academic search query
year_to	integer	Latest publication year
year_from	integer	Earliest publication year

Output

Schema · Response

{
  "type": "json",
  "example": {
    "data": {
      "query": "transformer architecture attention mechanism",
      "papers": [
        {
          "url": "https://arxiv.org/abs/1706.03762",
          "year": 2017,
          "title": "Attention Is All You Need",
          "authors": [
            "Vaswani",
            "Shazeer",
            "Parmar"
          ],
          "snippet": "We propose a new simple network architecture, the Transformer...",
          "position": 1,
          "citations": 125000
        },
        {
          "url": "https://arxiv.org/abs/1810.04805",
          "year": 2019,
          "title": "BERT: Pre-training of Deep Bidirectional Transformers",
          "authors": [
            "Devlin",
            "Chang",
            "Lee"
          ],
          "snippet": "We introduce BERT, a method for pre-training deep bidirectional...",
          "position": 2,
          "citations": 85000
        }
      ],
      "total_results": 45000
    },
    "meta": {
      "as_of": "2026-08-24T00:00:00.000Z",
      "sources": [
        "serpapi"
      ],
      "degraded": [],
      "attribution": [
        "Search data by SerpApi (serpapi.com)"
      ]
    }
  }
}