Prospect
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".
Examples
/apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees/apollo:prospect heads of marketing at e-commerce companies in Europe/apollo:prospect CTOs at fintech startups, 50-500 employees, New York/apollo:prospect procurement managers at manufacturing companies with 1000+ employees/apollo:prospect SDR leaders at companies using Salesforce and Outreach
Step 1 — Parse the ICP
Extract structured filters from the natural language description in "$ARGUMENTS":
Company filters:
- Industry/vertical keywords →
q_organization_keyword_tags - Employee count ranges →
organization_num_employees_ranges - Company locations →
organization_locations - Specific domains →
q_organization_domains_list
Person filters:
- Job titles →
person_titles - Seniority levels →
person_seniorities - Person locations →
person_locations
If the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.
Step 2 — Search for Companies
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search with the company filters:
q_organization_keyword_tagsfor industry/verticalorganization_num_employees_rangesfor sizeorganization_locationsfor geography- Set
per_pageto 25
Step 3 — Enrich Top Companies
Use mcp__claude_ai_Apollo_MCP__apollo_organizations_bulk_enrich with the domains from the top 10 results. This reveals revenue, funding, headcount, and firmographic data to help rank companies.
Step 4 — Find Decision Makers
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with:
person_titlesandperson_senioritiesfrom the ICPq_organization_domains_listscoped to the enriched company domainsper_pageset to 25
Step 5 — Enrich Top Leads
Credit warning: Tell the user exactly how many credits will be consumed before proceeding.
Use mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match to enrich up to 10 leads per call with:
first_name,last_name,domainfor each personreveal_personal_emailsset totrue
If more than 10 leads, batch into multiple calls.
Step 6 — Present the Lead Table
Show results in a ranked table:
Leads matching: [ICP Summary]
ICP Fit scoring:
- Strong — title, seniority, company size, and industry all match
- Good — 3 of 4 criteria match
- Partial — 2 of 4 criteria match
Summary: Found X leads across Y companies. Z credits consumed.
Step 7 — Offer Next Actions
Ask the user:
- Save all to Apollo — Bulk-create contacts via
mcp__claude_ai_Apollo_MCP__apollo_contacts_createwithrun_dedupe: truefor each lead - Load into a sequence — Ask which sequence and run the sequence-load flow for these contacts
- Deep-dive a company — Run
/apollo:company-intelon any company from the list - Refine the search — Adjust filters and re-run
- Export — Format leads as a CSV-style table for easy copy-paste