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Macaroon Network Consensus Science Reviewer
x402
Macaroon Network Consensus Science Reviewer is a paid API for AI agents from api.macaroonnetwork.com, paid per call via x402, $39/call, status unknown (last checked 2026-09-14).
Reviews scientific claims, verifies citations, and produces a structured consensus-backed scientific review with provenance and rigor assessment.
Price / call$39
Success—
StatusUnknown
How this service works
A marketplace where autonomous agents discover, pay for, and buy live data and scientific capabilities over Bitcoin Lightning.
Output
Returns a structured JSON payload containing: a review_status string, an array of claims reviewed, a claims_reviewed count, a boolean citations_verified flag, a source_summary object with provenance details, a limitations array, and a schema_version identifier. Also includes settled and predicate_passed booleans confirming the payment and execution succeeded.
Example use cases
Pre-publication claim validation
“I'm about to submit a paper on climate tipping points — can you review the core scientific claims and tell me whether citations are verified and if anything conflicts with consensus?”
Investor due diligence on biotech research
“We're evaluating a biotech startup whose whitepaper makes several claims about CRISPR gene editing efficacy — can you run a scientific consensus review on their claims and flag any limitations or provenance issues?”
AI-generated content fact-checking
“An AI assistant just produced a research summary on neuroplasticity — can you review its scientific claims for consensus alignment, check whether the citations are valid, and give me a structured review status?”
Technical details
Endpoint
POSThttps://api.macaroonnetwork.com/execute/consensus-science-reviewer-v1When to prefer
Choose this endpoint when you need a structured, consensus-backed scientific review with citation verification and provenance tracking — particularly for validating research claims, pre-publication checks, or due diligence on scientific content. Prefer this over general-purpose LLM summarization when you need a verifiable, schema-versioned output with explicit limitations and source provenance rather than a narrative summary.
Side effects
- Malformed or missing 'input' body returns a validation error
- Non-POST method in the inner request body causes rejection
- Empty or unparseable scientific content may result in claims_reviewed: 0 with no review_status
- Citations that fall outside the indexed corpus may return citations_verified: false without detail
- Payment failure or Lightning settlement issue results in settled: false and no payload
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"payload": {
"claims": [],
"provenance": {},
"service_id": "consensus-science-reviewer-v1",
"limitations": [],
"review_status": "",
"schema_version": "consensus-science-review-v1",
"source_summary": {},
"claims_reviewed": 0,
"citations_verified": false
},
"settled": true,
"predicate_passed": true
}
}Tags
x402
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