with relevant title and domain filters to identify the person, then proceed to enrichment.
Step 2 — Enrich the Person
Credit warning
Tell the user enrichment consumes 1 Apollo credit before calling.
Use
mcp__claude_ai_Apollo_MCP__apollo_people_match
with all available identifiers:
first_name
,
last_name
if name is known
domain
or
organization_name
if company is known
linkedin_url
if LinkedIn is provided
email
if email is provided
Set
reveal_personal_emails
to
true
If the match fails, try
mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search
with looser filters and present the top 3 candidates. Ask the user to pick one, then re-enrich.
Step 3 — Enrich Their Company
Use
mcp__claude_ai_Apollo_MCP__apollo_organizations_enrich
with the person's company domain to pull firmographic context.
Step 4 — Present the Contact Card
Format the output exactly like this:
[Full Name]
| [Title]
[Company Name] · [Industry] · [Employee Count] employees
Field
Detail
Email (work)
...
Email (personal)
... (if revealed)
Phone (direct)
...
Phone (mobile)
...
Phone (corporate)
...
Location
City, State, Country
LinkedIn
URL
Company Domain
...
Company Revenue
Range
Company Funding
Total raised
Company HQ
Location
Step 5 — Offer Next Actions
Ask the user which action to take:
Save to Apollo
— Create this person as a contact via
mcp__claude_ai_Apollo_MCP__apollo_contacts_create
with
run_dedupe: true
Add to a sequence
— Ask which sequence, then run the sequence-load flow
Find colleagues
— Search for more people at the same company using
mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search
with
q_organization_domains_list
set to this company
Find similar people
— Search for people with the same title/seniority at other companies