32 records

At how many employees does this federal employment law start to apply?

32 records, one fact each. Every value is transcribed from the source that states it and carries the sentence it was taken from: 3 sources across 3 sites, last checked 2026-09-16, due for re-check by 2027-08-04. Every record is listed below, each linking the page its value came from.

2 records where two or more sources state different values. Both sides are reproduced on the record page, each with its own source and quote.

About this data

Employee-count thresholds that trigger US federal employment laws (FMLA, ADA, COBRA, Title VII, ACA, ERISA and more). Each record is one law at one threshold, stating the trigger point, what the employer is required to do, and how employees are counted for that statute — including whether part-time, remote or FTE counts apply. Answers 'how many employees before [law] applies' and 'does FMLA count remote workers within 75 miles'.

The data

Where this came from

Every record above links the page it was taken from and quotes the sentence that states it. These are the 3 sources this dataset was assembled from.

• namely.comhttps://namely.com/blog/hr-compliance-50-employees/
• stratus.hrhttps://stratus.hr/resources/federal-law-breakdown-how-many-employees
• joinhomebase.comhttps://www.joinhomebase.com/blog/labor-laws-employee-count

Machine-readable

• data.jsonThe whole dataset — every record with its source URL and source quote.
• Open Knowledge Format bundleOne JSON object per line — every record's frontmatter and quoted span exactly as it is held here, in one fetch.
• data.csvThe same records as one flat table, for a spreadsheet or a dataframe. The last four columns are the source URL, the quoted sentence it was read from, the date we last checked it, and which columns are our reading rather than the page's words.
• How this is made and checkedWhat "verified against source" does and does not mean.

From your own code

Same records, same quotes, without scraping the page: refsource is on PyPI and npm. Each value comes back carrying the URL it was read from and the sentence on that page that states it — .source and .quote sit on the value itself rather than in a side channel, so the checking step is available instead of skipped.

pip install refsource
refsource lookup regulatory-threshold-triggers law=ACA

npx -y refsource lookup regulatory-threshold-triggers law=ACA

Set your AI assistant up to use this

Two files and no account. Put this in .mcp.json at the root of your project — Claude Code, Cursor, Windsurf, VS Code and Codex all read that file — and your assistant can look this dataset up instead of recalling it. The server is remote, keyless and read-only.

{
  "mcpServers": {
    "referencesource": {
      "type": "http",
      "url": "https://referencesource.org/mcp"
    }
  }
}

Add to Cursor · or, on the command line: claude mcp add --transport http referencesource https://referencesource.org/mcp --scope project

Then one line in the project's CLAUDE.md or AGENTS.md, so the assistant knows when to reach for it:

When a question needs "US federal employment law thresholds by employee count", call the referencesource MCP server at https://referencesource.org/mcp (tool `search_records`, dataset_slug `regulatory-threshold-triggers`) instead of answering from memory — every record it returns carries its source URL and a verbatim quote from that page.

What each tool does, and the servers built over single registers: Connect your AI assistant.

32 records. last verified against source 2026-09-16. due for re-check by 2027-08-04.

Licence. Facts extracted from vendor blog posts; thresholds are statutory and not copyrightable