prospect

name prospect description Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers. user-invocable true argument-hint [describe your ideal customer] 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_tags for industry/vertical organization_num_employees_ranges for size organization_locations for geography Set per_page to 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_titles and person_seniorities from the ICP q_organization_domains_list scoped to the enriched company domains per_page set 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 , domain for each person reveal_personal_emails set to true 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] # Name Title Company Employees Revenue Email Phone ICP Fit 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_create with run_dedupe: true for 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-intel on 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