US Employment Grounding Data

Overview

The data comes from the BLS public API: Current Population Survey series for the unemployment rate, participation rate, labor force, employed and unemployed, and Current Employment Statistics series for total nonfarm, private and eleven supersector payrolls. A collector runs on the 5th to 8th of each month to catch the first-Friday release and stores one record per month, with the newest also kept as the current report.

BLS publishes counts in thousands, and the API multiplies them by 1,000 so `totalEmployed`, `nonfarmPayrolls.total` and every `bySector` value are whole numbers of people. `summary.unemploymentRate` and `summary.laborForceParticipation` are percentages as published. `summary.jobsChange` is this month's total nonfarm payrolls minus last month's, so it is the headline jobs number, and `jobsChangeDirection` reads growth, decline or unchanged from its sign.

`topSector` is the sector with the largest employment that month; it measures size, not growth, so it rarely changes from month to month. The `formatted` strings abbreviate to K, M or B with a sign on `jobsChange`, so a loss reads -33.0K.

Omit `date` for the latest stored month. A paid `date` takes YYYY-MM; longer values such as 2025-10-01 are cut to their first seven characters. A month that BLS has not released, or that predates the stored series, returns a 404.

Live Test US Employment Grounding Data Source →

The tool

Once your client is connected to the VerveContext server, this appears in its tool list as USEmploymentGroundingData. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.

{
  "name": "USEmploymentGroundingData",
  "arguments": {}
}

Connecting

One server URL covers every source in the catalog, including this one. Authorization is OAuth: the client opens a browser once, and there is no key to paste into a config file.

{
  "mcpServers": {
    "vervecontext": {
      "url": "https://api.vervecontext.com/v1/mcp"
    }
  }
}

https://api.vervecontext.com/v1/mcp

Per-client setup — Claude, Cursor, VS Code, ChatGPT — is on the MCP setup page.

Arguments

These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made. Premium arguments are accepted on every plan but only take effect on plans that include them.

What the model gets back

The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.

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{
  "status": "ok",
  "error": null,
  "data": {
    "yearMonth": "2024-01",
    "year": 2024,
    "month": 1,
    "jobsChangeDirection": "growth",
    "summary": {
      "unemploymentRate": 3.7,
      "laborForceParticipation": 62.5,
      "totalEmployed": 161152000,
      "totalUnemployed": 6124000,
      "laborForce": 167276000,
      "jobsChange": 353000
    },
    "nonfarmPayrolls": {
      "total": 157245000,
      "private": 133567000
    },
    "bySector": {
      "mining": 645000,
      "construction": 8123000,
      "manufacturing": 12987000,
      "tradeTransportUtilities": 29456000,
      "information": 2987000,
      "financialActivities": 9234000,
      "professionalBusiness": 22876000,
      "educationHealth": 25678000,
      "leisureHospitality": 16789000,
      "otherServices": 5892000,
      "government": 23678000
    },
    "topSector": "Trade, Transport & Utilities",
    "formatted": {
      "totalEmployed": "161.15M",
      "totalUnemployed": "6.12M",
      "laborForce": "167.28M",
      "jobsChange": "+353.0K",
      "nonfarmPayrolls": "157.25M"
    }
  }
}

Response fields

Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.

Why ground on it

A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value in a shape you can check, which is the difference between an answer you can cite and one you have to hedge.

Point an evaluation at yearMonth: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.

Failure modes

Errors come back as tool errors carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.

A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.

Use cases

Economic Monitoring

Track monthly employment trends as a key economic indicator

Labor Market Analysis

Analyze employment by sector for industry research

Investment Research

Factor employment data into economic and market analysis

Policy Research

Study employment trends for economic policy research

Other ways to use US Employment Grounding Data

Set up US Employment Grounding Data on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.

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