Whistle plugs into Clay and Loopster as the college-athletics source

Clay and Loopster orchestrate enrichment from many vendors; neither specialises in college athletics. Whistle is the source for that slice: 1,085 NCAA athletic departments read from their official staff directories, with hire and departure signals. One HTTP step calling POST /api/v1/people/match returns, for each person in your table, their current title, school, division and conference, whether they have left, and on Pro+, Enterprise their email and phone. REST API access is on every Whistle plan; people matching needs Pro or above.

Data as of 4 Nov 2026.

Source: Whistle API reference at /api/docs; Clay facts from its public pricing page, checked October 31, 2026; Loopster facts from its public pricing page, checked October 31, 2026.

What does the HTTP step look like?

Method and URL

POST https://gowhistle.io/api/v1/people/match

Headers

Authorization: Bearer <your Whistle API key> and Content-Type: application/json

Body

A JSON object with a people array (up to 500 per call). Each person: id (your row id, echoed back), and either email, or name (or first_name + last_name) plus school, domain or title. school is also accepted as company, organization or account.

Options

dry_run: true previews match counts and cost without billing or returning staff fields; min_confidence (0.5 to 1, default 0.7) drops weaker matches.

API key

Settings → API keys in Whistle, on any plan. Send it only in the Authorization header; keys in the query string are rejected.

Billing

Each distinct matched record is one row against the plan's monthly rows; a record returned in the last 90 days is free. Response headers X-Whistle-Rows-Charged and X-Whistle-Rows-Remaining report usage. Plans and rows: pricing.

Sample request

Placeholder people; nothing here is a real person or address.

{
  "people": [
    {
      "id": "row-1",
      "name": "Jane Example",
      "school": "Example State University",
      "title": "Director of Athletics Marketing"
    },
    {
      "id": "row-2",
      "email": "sample.person@example.edu"
    },
    {
      "id": "row-3",
      "first_name": "Sam",
      "last_name": "Placeholder",
      "domain": "examplestateathletics.com"
    }
  ],
  "dry_run": false,
  "min_confidence": 0.7
}

Sample response

Shape only. Email and phone are masked unless the key's plan includes contact data (Pro+, Enterprise); email_status appears only with contact data.

{
  "dry_run": false,
  "skipped_rows": 0,
  "summary": {
    "total": 3,
    "matched": 2,
    "unmatched": 1,
    "ambiguous": 0,
    "school_unresolved": 0,
    "match_rate": 67,
    "distinct_contacts": 2,
    "contact_data_included": true,
    "graced_rows": 0,
    "billable_rows": 2,
    "from_allowance": 2,
    "overage_rows": 0,
    "amount_cents": 0,
    "overage_paused": false,
    "rows_charged": 2,
    "rows_remaining": 4998
  },
  "results": [
    {
      "id": "row-1",
      "input": {
        "name": "Jane Example",
        "email": null,
        "school": "Example State University",
        "domain": null,
        "title": "Director of Athletics Marketing"
      },
      "matched": true,
      "confidence": 0.96,
      "level": "high",
      "reason": "name_exact",
      "school_mismatch": false,
      "resolved_school": {
        "id": "example-state",
        "name": "Example State University",
        "method": "alias"
      },
      "alternatives": [],
      "staff": {
        "id": 123456,
        "name": "Jane Example",
        "title": "Associate AD for Marketing & Fan Experience",
        "department": "External Relations",
        "office": null,
        "school_id": "example-state",
        "school_name": "Example State University",
        "division": "Division I",
        "conference": "Example Conference",
        "email": "jane.example@example.edu",
        "phone": "555-0100",
        "email_status": "verified",
        "linkedin_url": null,
        "bio_url": "https://examplestateathletics.com/staff-directory/jane-example/1",
        "functional_area": "external",
        "buyer_persona": null,
        "last_scraped_at": "2026-10-29T06:12:00.000Z",
        "departed": false,
        "departed_at": null,
        "departure_source": null
      }
    },
    {
      "id": "row-2",
      "input": {
        "name": null,
        "email": "sample.person@example.edu",
        "school": null,
        "domain": null,
        "title": null
      },
      "matched": true,
      "confidence": 1,
      "level": "high",
      "reason": "email_exact",
      "school_mismatch": false,
      "resolved_school": {
        "id": "example-state",
        "name": "Example State University",
        "method": "email_domain"
      },
      "alternatives": [],
      "staff": {
        "id": 123457,
        "name": "Sample Person",
        "title": "Director of Development",
        "department": "Athletics Development",
        "office": null,
        "school_id": "example-state",
        "school_name": "Example State University",
        "division": "Division I",
        "conference": "Example Conference",
        "email": "sample.person@example.edu",
        "phone": null,
        "email_status": "accept_all",
        "linkedin_url": null,
        "bio_url": null,
        "functional_area": "external",
        "buyer_persona": null,
        "last_scraped_at": "2026-10-29T06:12:00.000Z",
        "departed": true,
        "departed_at": "2026-09-14T03:05:00.000Z",
        "departure_source": "signal"
      }
    },
    {
      "id": "row-3",
      "input": {
        "name": "Sam Placeholder",
        "email": null,
        "school": null,
        "domain": "examplestateathletics.com",
        "title": null
      },
      "matched": false,
      "confidence": 0,
      "level": "none",
      "reason": "no_person_match",
      "school_mismatch": false,
      "resolved_school": {
        "id": "example-state",
        "name": "Example State University",
        "method": "domain"
      },
      "alternatives": [],
      "staff": null
    }
  ]
}

Which response fields should I map to columns?

How do I set it up in Clay or Loopster?

• Clay: add an “HTTP API” enrichment column (Clay lists HTTP API integrations on its Growth plan and above), choose POST, paste the URL and headers above, and build the body from your table's columns: {"people": [{"id": {{row_id}}, "name": {{Full Name}}, "school": {{Company}}, "title": {{Title}}}]}. Map the response paths in the table above to new columns.
• Loopster: add an HTTP request step to the table with the same method, URL, headers and body, then map results[0].staff.* to columns. Loopster's API and MCP server are included on every plan per its pricing page; the Whistle call runs inside its workflow like any other vendor step.
• One person per call is simplest in a table tool; batch up to 500 people per request from a script or a scheduled job when you refresh a whole list.
• Start with dry_run: true on a sample of rows to see the match rate and the rows that will be billed, then run for real.

What are Clay and Loopster, and how does Whistle differ?

Frequently asked questions