How to Find Decision Makers at a Company (in ChatGPT)

TL;DR

• You connect LeadOcean to ChatGPT once, then ask for the C-level and VP people at any company domain.
• It takes about 15 minutes the first time and about 5 after that.
• A full run spends 12 LeadOcean records: 10 for the search, 2 for enriching the people you pick. Counting is free.

Prerequisites

• A LeadOcean account. Start free: no card, 1,000 records one-off, work email required.
• A ChatGPT account that may add a custom MCP server. OpenAI's connection docs, October 2026, say account and workspace policies apply, so ask your admin if the option is missing.
• Optional, for the script in Step 5: $LEADOCEAN_API_KEY in your shell and curl.

You paste no API key into ChatGPT. The MCP server uses OAuth sign-in.

Step 1: Connect LeadOcean to ChatGPT

Add the server URL as a custom MCP server and sign in. The full walkthrough is on Connect LeadOcean to ChatGPT. The short version:

ChatGPT Plugins > plus button > Add custom MCP server
Name:            LeadOcean
Server URL:      https://api.leadocean.io/mcp
Authentication:  OAuth
Then:            I understand and want to continue > Create as a plugin

Install the plugin, sign in to LeadOcean and approve. The server exposes 13 tools. Seven of them are free, and leadocean_count_leads is the one you use next.

Step 2: Size the decision makers before you pull anyone

Ask for a count first. Counting costs 0 records, so you can tune the filters as often as you like. In a new chat, type @, select the LeadOcean plugin and send:

Use the LeadOcean plugin only. How many C-Team and VP people in
Sales & Business Development are in the US? Count only. Do not pull anyone.

ChatGPT calls leadocean_count_leads with these arguments:

{
  "jobLevel": ["C-Team", "VP"],
  "jobFunction": ["Sales & Business Development"],
  "country": ["US"],
  "facets": false
}

The values are exact strings. If ChatGPT guesses one, tell it to call leadocean_list_enum_values first.

Step 3: Search one company by domain

Now point the same filters at one company. Use the domain, not the company name. Names match loosely, and a domain matches the employer exactly.

Use the LeadOcean plugin only. Find decision makers at acme.com:
C-Team and VP level people. Call leadocean_search_leads with limit 10.
Show title, level and whether an email is on file. Do not enrich anyone yet.

The tool call looks like this:

{
  "domain": ["acme.com"],
  "jobLevel": ["C-Team", "VP"],
  "limit": 10
}

This spends up to 10 records, one per person returned. A small company returns fewer. Swap acme.com for the domain you care about. Add jobFunction to narrow to one department, such as Sales & Business Development.

Step 4: Enrich the two people you want

Search rows carry a person_id. Enriching by that id is how you reach a person with no LinkedIn URL. Ask ChatGPT to enrich only the people you picked:

Enrich the CRO and the VP of Marketing from that list with
leadocean_get_lead using their person_id. Show the work email and its
email_status. Tell me how many records this call spent.

Each enrichment costs 1 record. A person we hold nothing for is a miss, and a miss costs nothing. Read email_status before you send anything. Bounced emails are not refunded.

Step 5: Run the same search from a script

ChatGPT calls the same API you can call yourself. Use this when you want the list in a file instead of a chat. The search is a flat JSON body, and filters are arrays.

curl -s -X POST https://api.leadocean.io/v1/people/search \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"domain": ["acme.com"], "jobLevel": ["C-Team", "VP"], "limit": 10}'

Enrich one person with the id from the search row:

curl -s -X POST https://api.leadocean.io/v1/people/enrich \
  -H "x-api-key: $LEADOCEAN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"person_id": "1234567890", "reveal_email": true}'

Enrich takes exactly one of linkedin_url, email, phone or person_id. To list everyone we hold at a company instead, use GET /v1/companies/acme.com/people, which bills one record per person returned.

What you get

The count in Step 2 is a live result. We ran it on 2026-10-21 through the LeadOcean MCP server (trimmed):

{
  "total": 279115,
  "totalIsExact": true,
  "totalAll": 954471,
  "distinctCompanies": 106332,
  "credits": 0,
  "defaults": "Counted only addresses worth sending to (emailStatus: verified or catch_all_valid or catch_all), which is this tool's default."
}

That is 279,115 mailable C-Team and VP sales leaders in the US, with a verified, catch_all_valid or catch_all email. It cost 0 records. With no email condition the same filters match 954,471 people (totalAll). A REST count with count=true caps meta.total at 100,000, so use the MCP tool for a number this size.

For one company, the people come back in the response shape from the API reference. These are placeholder values, not a real person:

{
  "success": true,
  "data": [
    {
      "profile_data": { /* name, title, company for Jane Doe at acme.com */ },
      "contact_data": {
        "has_email": true,
        "has_phone": true,
        "email_status": "verified",
        "email_type": "work"
      },
      "meta": { "source_ids": { "person_id": "1234567890" } }
    }
  ],
  "meta": { "count": 1, "nextCursor": null }
}

Search shows flags such as has_email, not the address. Enrich with reveal_email set to true returns contact_emails and contact_phones, a phone looking like +1 415 555 0133, at no extra record.

Troubleshooting

FAQ