# AnswerPool Technology Momentum

> 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.

## Facts

- Endpoint: GET https://answerpool.io/v1/technology/momentum
- Price: $0.05/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-16
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/answerpool-technology-momentum-b6254297
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_DjaEcsCEa7rhpV9saFdqn

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability answerpool-technology-momentum-b6254297
```

Example prompt: What's the research momentum score for photonic computing right now — is it accelerating, stable, or declining, and what are the main drivers?

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "required": {
   "type": "string"
  },
  "properties": {
   "type": "string"
  }
 }
}
```

## Response schema (JSON Schema)

```json
{
 "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
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/answerpool-technology-momentum-b6254297/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from answerpool.io](https://www.zero.xyz/host/answerpool.io/llms.txt)