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AnswerPool Technology Momentum
x402
AnswerPool Technology Momentum is a paid API for AI agents from answerpool.io, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-16).
Computes a momentum score and trend analysis for a technology topic using citation growth, publication volume, and institutional adoption from open research data.
Price / call$0.05
Success—
StatusUnknown
How this service works
AnswerPool turns SEC EDGAR, the Federal Register, USAspending, NIH, BLS and OpenAlex into structured JSON answers that AI agents and developers fetch in one call. 69 endpoints free, no account; derived analyses $0.02–$0.05 per call by card credits or USDC (x402). MCP server, full provenance.
Output
Returns a structured JSON object containing: a momentum_score (0–1), a trend_label (e.g. 'accelerating'), citation growth rates at 1y/3y/5y, research growth rates at 1y/3y/5y, an acceleration_score, institution_growth, matched topic names with share and work counts, leading researchers and institutions, important recent works, key drivers, risks, warnings, confidence score, methodology version, and data freshness timestamps.
Example use cases
Emerging tech screening for investors
“I'm evaluating whether solid-state batteries are worth investing in — can you pull the current momentum score and tell me whether citation growth is accelerating, who the leading research institutions are, and what the main risks are?”
Competitive technology landscape report
“I need to compare research momentum for neuromorphic computing versus conventional GPU architectures — start with neuromorphic computing and give me the trend label, momentum score, and key drivers.”
Science policy briefing on climate tech
“Can you look up the research momentum for direct air capture of CO2? I want to know if academic activity is accelerating, what the top institutions are, and whether there are any warning signs in the data.”
Technical details
Endpoint
GEThttps://answerpool.io/v1/technology/momentumWhen to prefer
Use this endpoint when you need quantified, evidence-backed momentum analysis of a specific technology or research field grounded in peer-reviewed publication and citation data. Prefer it over general web search or LLM reasoning when you need a reproducible score with provenance and methodology versioning. Ideal for investment screening, competitive technology benchmarking, science policy briefs, or R&D prioritization where you need citable, structured intelligence rather than anecdotal trend observations.
Side effects
- Topic query matches no OpenAlex topics — returns empty matched_topics and low confidence
- Ambiguous topic string matches multiple unrelated fields — results may blend signals from unrelated domains
- Very new or niche topics may have insufficient evidence_count, lowering confidence and reliability
- Stale data if data_as_of lags significantly behind computed_at
- Payment failure via x402 protocol returns 402 before any data is delivered
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| required | string | |
| properties | string |
Output
Schema · Response
{
"type": "json",
"example": {
"risks": [
"fabrication cost"
],
"topic": "photonic computing",
"warnings": [],
"rationale": "Publication and top-decile citation growth accelerated over the last 3 years.",
"result_id": "res_a1b2c3",
"confidence": 0.72,
"data_as_of": "2026-08-24",
"computed_at": "2026-08-30T18:00:00Z",
"key_drivers": [
"AI inference energy limits"
],
"query_match": {
"mode": "phrase",
"focus_topic_ids": [
"T10412"
],
"total_works_10y": 4210
},
"trend_label": "accelerating",
"evidence_count": 412,
"matched_topics": [
{
"name": "Photonic and Optical Computing",
"share": 0.69,
"works": 2914,
"topic_id": "T10412"
}
],
"momentum_score": 0.81,
"schema_version": "1",
"citation_growth": {
"1y": 0.18,
"3y": 0.7,
"5y": 1.2
},
"research_growth": {
"1y": 0.21,
"3y": 0.86,
"5y": 1.7
},
"acceleration_score": 0.64,
"institution_growth": 0.35,
"leading_researchers": [
{
"id": "A5012345678",
"name": "J. Doe",
"recent_works": 34
}
],
"methodology_version": "1.2.0",
"leading_institutions": [
{
"id": "I63966007",
"name": "Massachusetts Institute of Technology",
"recent_works": 210
}
],
"important_recent_works": [
{
"id": "W4400000001",
"year": 2026,
"title": "On-chip photonic tensor cores",
"cited_by": 89
}
],
"citation_growth_as_of_year": 2024
}
}Tags
x402
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