Recommended Contacts

Get ML-ranked contact recommendations at a target company, personalized to your ZoomInfo usage and CRM data.

Input

The user will provide via $ARGUMENTS:

• A company name, domain, or ZoomInfo company ID (required)
• Optionally: a use case — "prospecting", "deal acceleration", or "renewal" (defaults to PROSPECTING)
• Optionally: how many results they want (defaults to 25, max 100)

Workflow

1. Lookup metadata first — before calling any other MCP tool, use lookup to load reference data for any fields relevant to the request. Use the returned id values (not display names) in all subsequent API calls. This ensures accurate parameter resolution and result interpretation.

2. Resolve the company if the user provided a name or domain:

   • Use search_companies with companyName or companyWebsite to find the company — use lookup id values for any filters.
   • Extract the ZoomInfo company ID from the result.

3. Enrich the company using enrich_companies with the resolved companyId to get firmographic context (industry, size, revenue, business model). This context is used to interpret the recommendations.

4. Map the use case to the correct enum value:

   • "prospecting" or default → PROSPECTING (based on contacts you've viewed, copied, or exported on the ZoomInfo platform; has cold-start support)
   • "deal acceleration" or "new business" → DEAL_ACCELERATION (based on contacts in closed-won CRM opportunities for new business)
   • "renewal", "growth", or "expansion" → RENEWAL_AND_GROWTH (based on contacts in closed-won CRM opportunities for renewals)

5. Get recommendations using get_recommended_contacts with:

   • ziCompanyId: the resolved ZoomInfo company ID
   • useCaseType: the mapped enum value
   • pageSize: user-specified count or 25

6. Enrich the top contacts using enrich_contacts on the top 10 results (batch of 10) to get full contact details including email, direct phone, and accuracy scores.

Output Format

Target Company

One-line summary: [Company Name] — [Industry], [Employee Count] employees, [Revenue], [HQ Location]

Use Case

State which use case was used and what it means:

• PROSPECTING: "Recommendations based on contacts similar to those you've recently viewed, copied, or exported in ZoomInfo."
• DEAL_ACCELERATION: "Recommendations based on contact patterns from your CRM's closed-won new business deals."
• RENEWAL_AND_GROWTH: "Recommendations based on contact patterns from your CRM's closed-won renewal deals."

Recommended Contacts

Rank	Name	Title	Department	Management Level	Email	Direct Phone	Accuracy	Score
1
2

For each contact, use the meta field from the recommendation response to explain WHY they were recommended. The meta describes the reference person the recommendation was based on. Present this as a "Why Recommended" note below the table or as an additional column.

Recommendation Analysis

Group the recommended contacts by pattern:

• By Department: Which departments are most represented? (e.g., "8 of 25 are in Sales, 6 in Marketing")
• By Seniority: What management levels dominate? (e.g., "Heavily weighted toward Director and VP")
• By Function: What job functions appear most? (e.g., "Strong signal toward revenue-facing roles")

Use the resolved lookup values to categorize accurately — do not guess department or management level labels.

Engagement Priority

Rank the top 5 contacts to engage first, with reasoning:

• Who has the highest combined relevance (recommendation score) and reachability (accuracy score)?
• Who is the likely entry point vs. the likely decision-maker?
• Suggested outreach sequence

Next Steps

• Use /zoominfo:enrich-contact to deep-dive on any specific person
• Use /zoominfo:find-buyers if you need to filter by specific persona criteria beyond what recommendations provide
• If recommendations are sparse, note that PROSPECTING recommendations improve as you use ZoomInfo more (view, copy, export contacts). DEAL_ACCELERATION and RENEWAL_AND_GROWTH require CRM integration.

Important Notes on Scores

• The score (general similarity) and reRankingScore (propensity-adjusted) are not directly comparable to each other
• Higher scores indicate stronger fit but do not guarantee response rates
• Recommendations refresh daily based on your latest platform and CRM activity