How to Get B2B Data for GTM Teams (with the LeadOcean API)

TL;DR

• GTM teams need four things from a data source: a free size check, a small test pull, verified emails for the best rows and a bulk export. Each is one request below.
• It takes about 30 minutes. The example spends about 35 of your 1,000 free records: 25 for the pull, 10 for email reveals.
• Counts, filter lookups and enum lookups cost nothing. Only people you receive count as records.

Prerequisites

• A LeadOcean account. The free plan has 1,000 records for life, no card and 5 requests a second.
• An API key from app.leadocean.io, exported as $LEADOCEAN_API_KEY.
• curl and jq. Python 3 is optional for the loop in step 3.
• A written ICP: job level, function, country and company size.
• A paid plan only for step 4. Bulk CSV export is not on Free. It is included on Solo ($97 a month) and Agency ($497 a month).

export LEADOCEAN_API_KEY="paste-from-app-leadocean-io"
curl -s https://api.leadocean.io/v1/account -H "x-api-key: $LEADOCEAN_API_KEY"

The account call is free. It shows your plan and the records you have left.

Step 1: Size the list before you spend anything

A people search with count=true and limit=1 returns a total and no rows. It costs nothing. Send count as a query parameter, not in a JSON body.

curl -s -G https://api.leadocean.io/v1/people/search \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  --data-urlencode "jobLevel=VP" \
  --data-urlencode "jobFunction=Sales & Business Development" \
  --data-urlencode "country=US" \
  --data-urlencode "emailStatus=verified,catch_all_valid,catch_all" \
  --data-urlencode "count=true" \
  --data-urlencode "limit=1" | jq '.meta | {total, totalIsExact, countOnly, distinctCompanies}'

meta.total stops at 100,000. If totalIsExact is false, treat the number as a floor. The same filters in the MCP tool leadocean_count_leads return an uncapped count with breakdowns.

Sizing companies works the same way. GET /v1/companies/count takes the company-search filters and returns an exact total with breakdowns by industry, headcount band, HQ country, HQ state and NAICS.

curl -s -G https://api.leadocean.io/v1/companies/count \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  --data-urlencode "industry=Software Development" | jq '.data | {total, facets}'

Enum filters are exact strings. Look them up first with GET /v1/enums/job_function or the leadocean_list_enum_values tool.

Step 2: Pull a small batch of people

Pull 25 rows to check fit before you commit volume. The search body is flat JSON, filters are arrays, limit runs 1 to 100 and paging uses a cursor.

curl -s https://api.leadocean.io/v1/people/search \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "jobLevel": ["VP"],
    "jobFunction": ["Sales & Business Development"],
    "country": ["US"],
    "emailStatus": ["verified", "catch_all_valid", "catch_all"],
    "limit": 25
  }' > people.json

jq '{rows: (.data | length), nextCursor: .meta.nextCursor}' people.json

Search rows are thin: profile, current role and email flags. Each row carries a person_id at meta.source_ids.person_id. Step 3 uses it.

To start from a target account instead, call GET /v1/companies/acme.com/people. It lists people at one domain and stops at 10,000 rows.

Step 3: Reveal work emails for the best rows

Enrich takes exactly one of linkedin_url, email, phone or person_id. There is no name plus domain lookup, so search first and enrich by person_id. Set reveal_email to true to get the address. It costs no extra record.

import json, os, requests

H = {"x-api-key": os.environ["LEADOCEAN_API_KEY"]}
rows = json.load(open("people.json"))["data"][:10]

with open("enriched.jsonl", "a") as out:
    for r in rows:
        pid = r["meta"]["source_ids"]["person_id"]
        res = requests.post("https://api.leadocean.io/v1/people/enrich",
                            headers=H, json={"person_id": pid, "reveal_email": True})
        if res.status_code == 404:   # nobody matched, costs nothing
            continue
        res.raise_for_status()
        out.write(json.dumps(res.json()) + "\n")

Ten reveals spend 10 records. An enrichment that finds nothing is a 404 and costs nothing. Since October 2026 the response also carries career history: past roles, education and certifications. Past employers have no company_id yet.

Read email_status before you send. LeadOcean does not refund or credit back bounced emails.

Step 4: Export the full list for your sequencer or CRM

Once the 25-row sample looks right, export. POST /v1/exports takes the same filters as search. It takes up to 50,000 rows a request and 500,000 a day. This needs Solo or Agency.

curl -s https://api.leadocean.io/v1/exports \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "US sales VPs, mailable",
    "filters": {
      "jobLevel": ["VP"],
      "jobFunction": ["Sales & Business Development"],
      "country": ["US"],
      "emailStatus": ["verified", "catch_all_valid", "catch_all"]
    },
    "limit": 5000
  }'

You can also run the same export in the app at app.leadocean.io. The Exports page has a filter builder, a column picker, the record price before you start and a CSV download. Load the file into your own sequencer or CRM. LeadOcean supplies the data. It has no sequencer, dialer, CRM or Chrome extension.

Step 5: Let the team ask in plain language

Ops and sales people who do not write curl can use the hosted MCP server. It uses OAuth sign-in, so no key is pasted. Add it in Claude Code with one command (Claude Code MCP docs).

claude mcp add --transport http leadocean https://api.leadocean.io/mcp

Then ask for a count first. This prompt spends no records.

Use leadocean_count_leads for VPs in Sales & Business Development in the US.
Do not search or look up any person. Return the total and the top 5 industries.

leadocean_count_leads, leadocean_list_filters and leadocean_list_enum_values are free. leadocean_search_leads, leadocean_get_lead and leadocean_export_leads spend records, so tell the agent to stop and ask before it calls them.

Choosing the route

Use the API when a script or an enrichment job calls the data. Use the app Exports page when a RevOps person needs a CSV today and wants the record price shown first. Use MCP when the question is "how big is this segment" and a person is typing it.

Keep one rule across all three: size first, sample 25, then scale. A bad enum value or a wrong country filter costs you 25 records at the sample stage, not 5,000 at the export stage.

What you get

This is a live free count run on 2026-10-21 with leadocean_count_leads (jobLevel VP, jobFunction Sales & Business Development, country US). It filters on mailable addresses (emailStatus verified, catch_all_valid or catch_all) and spent 0 records.

{
  "total": 129494,
  "totalIsExact": true,
  "totalAll": 350517,
  "distinctCompanies": 58121,
  "credits": 0
}

totalAll is the same filters with no email filter. Pulling 25 of those rows plus 10 reveals is 35 records, 3.5% of the free allowance.

A search row has this shape (response shape from the API reference, placeholder values):

{
  "success": true,
  "data": [
    {
      "profile_data": {
        "profile_full_name": "Jane Doe",
        "profile_headline": "VP of Sales at Acme"
      },
      "contact_data": {
        "has_email": true,
        "email_status": "verified",
        "email_type": "work",
        "contact_current_experiences": [
          { "company_name": "Acme", "company_domain": "acme.com", "job_title": "VP of Sales" }
        ]
      },
      "meta": { "source_ids": { "person_id": "1234567890" } }
    }
  ],
  "meta": { "count": 25, "credits": 25, "nextCursor": "eyJ..." }
}

Troubleshooting

FAQ