prospect

Runs a full ICP-to-leads pipeline: the user describes an ideal customer in plain English and receives a ranked table of enriched decision-maker leads with emails and phone numbers. It is user-invocable and accepts the ICP description as arguments, with examples like a VP of Engineering at Series B+ SaaS companies in the US or heads of marketing at e-commerce companies in Europe.

The workflow is a sequence of steps built on Apollo MCP tools. Step 1 parses the natural-language ICP into structured company filters (industry keyword tags, employee count ranges, locations, specific domains) and person filters (job titles, seniorities, person locations), asking one or two clarifying questions if the description is vague; at minimum it needs a title or role plus an industry or company size. Step 2 searches for companies with apollo_mixed_companies_search using the company filters at 25 results per page. Step 3 enriches the top ten companies via apollo_organizations_bulk_enrich to reveal revenue, funding, headcount, and firmographics for ranking. Step 4 finds decision makers with apollo_mixed_people_api_search scoped to the enriched company domains. Step 5 enriches the top leads with apollo_people_bulk_match, up to ten per call with personal emails revealed, batching into multiple calls when needed, and warns the user of credit consumption first.

Step 6 presents a ranked lead table with name, title, company, employees, revenue, email, phone, and an ICP Fit rating of Strong, Good, or Partial based on how many of the title, seniority, company size, and industry criteria match, plus a summary of leads, companies, and credits consumed. Step 7 offers next actions: save all leads to Apollo via apollo_contacts_create with deduplication, load them into a sequence, deep-dive a company with /apollo:company-intel, refine the search, or export the leads as a CSV-style table. Credit warnings are surfaced before enrichment steps that consume credits.