StationFX Job Openings Total Nonfarm

x402

StationFX Job Openings Total Nonfarm is a paid API for AI agents from stationfx.com, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-02).

Returns monthly U.S. total nonfarm job openings data from the JOLTS survey, including trend statistics and z-scores, to measure labor market demand.

Price / call$0.005

Seller volume →—

Success—

StatusUnknown

How this service works

Total unfilled job positions across the U.S. economy from the JOLTS survey. Measures labor demand. Use to assess labor market tightness, compare with unemployment for supply/demand balance, and feed into the Job Openings to Unemployed ratio signal. Monthly frequency.

Output

Returns a time-series array of monthly JOLTS job openings observations, each containing the raw value, month-over-month and year-over-year absolute and percentage changes, 12-month and 5-year z-scores, 5-year percentile rank, 3-month and 12-month rolling averages, and an above-trend binary flag.

Example use cases

Labor market tightness dashboard

“Get me the last 24 months of JOLTS job openings data with month-over-month and year-over-year changes so I can track how labor market demand has evolved since 2022.”

Fed policy signal research

“Pull U.S. total nonfarm job openings from January 2020 through today, including z-scores and percentile ranks, so I can assess where labor demand sits relative to historical norms for a Fed policy analysis.”

Job openings to unemployed ratio signal

“Fetch the most recent job openings reading from StationFX — just the latest month's value and its 5-year percentile rank — so I can compute the Job Openings to Unemployed ratio against current unemployment figures.”

Technical details

Endpoint

GEThttps://stationfx.com/economic-data/labor/job-openings-total-nonfarm?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need authoritative, pre-computed JOLTS total nonfarm job openings data with built-in statistical context (z-scores, percentile ranks, trend flags) rather than raw BLS data requiring your own processing. Ideal for macro signal generation, labor market dashboards, and ratio computations requiring historical trend context.

Side effects

Invalid date range (date_from after date_to) returns error
Requesting future dates beyond latest available monthly release returns empty or partial data
Unsupported fmt enum value returns validation error
Missing required 'input' object returns 400-level error
Data not yet released for most recent month due to BLS publication lag

Input

Schema · Request body

Field	Type	Description
inputrequired	object
output	object

Output

Schema · Response

{
  "type": "json",
  "schema": {
    "type": "object",
    "properties": {
      "data": {
        "type": "array",
        "items": {
          "type": "object",
          "properties": {
            "date": {
              "type": "string",
              "description": "Observation date YYYY-MM-DD"
            },
            "value": {
              "type": "number",
              "description": "Raw observed value in series units"
            },
            "mom_pct": {
              "type": "number",
              "description": "Month-over-month % change"
            },
            "yoy_pct": {
              "type": "number",
              "description": "Year-over-year % change"
            },
            "zscore_5y": {
              "type": "number",
              "description": "Z-score relative to trailing 5 years"
            },
            "mom_change": {
              "type": "number",
              "description": "Month-over-month absolute change"
            },
            "yoy_change": {
              "type": "number",
              "description": "Year-over-year absolute change"
            },
            "zscore_12m": {
              "type": "number",
              "description": "Z-score relative to trailing 12 months"
            },
            "above_trend": {
              "type": "integer",
              "description": "1 if value is above long-run trend, else 0"
            },
            "pct_rank_5y": {
              "type": "number",
              "description": "Percentile rank over trailing 5 years (0-100)"
            },
            "rolling_3m_avg": {
              "type": "number",
              "description": "3-month rolling average"
            },
            "rolling_12m_avg": {
              "type": "number",
              "description": "12-month rolling average"
            },
            "trend_direction": {
              "type": "integer",
              "description": "Trend: 1 rising, -1 falling, 0 flat"
            }
          }
        },
        "description": "Observations ordered by date ascending"
      },
      "meta": {
        "type": "object",
        "description": "Series metadata: source_key (FRED series ID), units, frequency (D/W/M/Q/A), date_from, date_to, fields"
      }
    },
    "description": "Station f(x) response with metadata and pre-computed derived metrics"
  },
  "example": {
    "data": [
      {
        "date": "2024-01-01",
        "value": 5.33,
        "mom_pct": 0,
        "yoy_change": 0.5,
        "zscore_12m": 1.2,
        "trend_direction": 1
      }
    ],
    "meta": {
      "frequency": "M",
      "source_key": "JTSJOL"
    }
  }
}

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