Enrich Person

Enrich a person using their first name, last name, and company domain, or simply their LinkedIn profile URL. You can also reverse lookup a person from their email address to find matching profile and contact data.

What it does

• Enriches a person from a first name, last name and company domain — the most common combination.
• Or from a linkedin_url on its own, which is the most accurate anchor.
• Or in reverse, from an email on its own, to find the profile and contact data behind an address.
• Returns contact details, job title, company info, and more.
• Every field is optional, but you must pass at least one. Every extra identifier you pass narrows the match.
• One call, 3 credits, however many identifiers you send.

It is the general-purpose person lookup. If you have a LinkedIn URL and you want full profile detail — work history, education, skills — use linkedin_profile_enrich instead.

Use cases

• Enrich a list of LinkedIn profiles. Hand your client a set of profile URLs and ask for the record behind each one. linkedin_url is the strongest anchor and works on its own — no name or company needed alongside it.
• Reverse lookup from an email. An address from a form fill, a webinar export, or a shared inbox is enough by itself. Pass email and get back the matching profile and contact data — who they are, their title, and their employer.
• Resolve a name-and-domain list. First name, last name, and company domain is the most common outbound starting point. Run a CSV through it and get a person record back on every row that resolves.
• Clean an imported list. Fill in title, company, and LinkedIn URL across rows that arrived with only partial data, in one pass rather than one action per field.
• Identify who is behind an inbound signup. Turn a raw address from a form or an inbox into a title and an account before it hits routing.
• Confirm a title before you write. Personalisation built on a stale title lands worse than no personalisation at all.

How to use it

Enrich Jane Doe at stripe.com

Enrich linkedin.com/in/janedoe

Enrich the person behind jane@stripe.com

Enrich these 30 rows. Each has a first name, last name and company. Report how
many credits you used and which rows came back empty.

Example prompts

Best prompting practices

• Anchor on the strongest identifier you have. linkedin_url beats email, and email beats a name. Tell your client which column is the anchor so it does not lead with the weakest field.
• Always pass the company with a name. A name on its own matches the wrong Jane Doe more often than you would like. domain and organization_name are free and they fix most of those.
• Prefer domain over organization_name. Company names are ambiguous and inconsistently formatted across sources; a domain is unique. Pass both when you have both.
• Let a LinkedIn URL stand alone. “Enrich these profile URLs, do not guess the company” — a wrong extra identifier can push the match away from the right person rather than toward it.
• Say when you want a reverse lookup. “These are just email addresses — find the person behind each one” stops your client from asking you for names it does not need.
• Say what you want back. “Just the job title and company” keeps the client from dumping the whole record into the chat; “return as CSV with these columns” keeps it from summarizing the record away.
• Spot-check before you commit. “Run the first five, show me the matches, then wait.” A wrong match still bills credits, and on a list of common names you want to see the hit quality before row 300.
• Deduplicate and suppress first. Filtering the rows you already own is free; enriching them is 3 credits each.
• Cap the batch. “Stop after 50 and report credits used” keeps a long list from running away with your balance.
• Forbid invention. “If no record is found, leave the row blank and mark it not found — do not infer the title from the company.” An invented title reads exactly like a real one.
• Ask for the profile tool by name when you want work history or education — otherwise your client will settle for this one and give you a thinner record.

Output

{
  "first_name": "Jane",
  "last_name": "Doe",
  "job_title": "VP Sales",
  "email": "jane@stripe.com",
  "organization_name": "Stripe"
}

Credits and limits

3 credits per call. Run check_credits (free) before a large batch.

Parameters

Every field is optional, but the call needs at least one. Pass every identifier you hold, not just the anchor — the extras cost nothing and are what stop a wrong match.

Related tools

Next steps

• All MCP tools — the full catalogue with credit costs
• AI for Sales Reps — workflows that chain this tool into a list build

Keep learning

Two courses take these tools past the reference page — clustered by GTM job, then chained into workflows.