From zoominfo
Get AI-powered contact recommendations at a target company based on your ZoomInfo interaction history. Provide a company name or domain and optionally a use case.
npx claudepluginhub zoominfo/zoominfo-mcp-plugin --plugin zoominfoThis skill uses the workspace's default tool permissions.
Get ML-ranked contact recommendations at a target company, personalized to your ZoomInfo usage and CRM data.
Builds targeted company and contact lists using Common Room Prospector for net-new prospects or existing accounts with signals. Clarifies object types and refines iteratively.
Researches B2B leads and decision makers by role, company, location; enriches organizations by domain and people by email using Apollo.io API.
Generates ranked tables of enriched decision-maker leads (emails/phones) from ICP descriptions using Apollo company/people searches and bulk enrichment.
Share bugs, ideas, or general feedback.
Get ML-ranked contact recommendations at a target company, personalized to your ZoomInfo usage and CRM data.
The user will provide via $ARGUMENTS:
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.
Resolve the company if the user provided a name or domain:
search_companies with companyName or companyWebsite to find the company — use lookup id values for any filters.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.
Map the use case to the correct enum value:
PROSPECTING (based on contacts you've viewed, copied, or exported on the ZoomInfo platform; has cold-start support)DEAL_ACCELERATION (based on contacts in closed-won CRM opportunities for new business)RENEWAL_AND_GROWTH (based on contacts in closed-won CRM opportunities for renewals)Get recommendations using get_recommended_contacts with:
ziCompanyId: the resolved ZoomInfo company IDuseCaseType: the mapped enum valuepageSize: user-specified count or 25Enrich 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.
One-line summary: [Company Name] — [Industry], [Employee Count] employees, [Revenue], [HQ Location]
State which use case was used and what it means:
| Rank | Name | Title | Department | Management Level | 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.
Group the recommended contacts by pattern:
Use the resolved lookup values to categorize accurately — do not guess department or management level labels.
Rank the top 5 contacts to engage first, with reasoning:
/zoominfo:enrich-contact to deep-dive on any specific person/zoominfo:find-buyers if you need to filter by specific persona criteria beyond what recommendations providescore (general similarity) and reRankingScore (propensity-adjusted) are not directly comparable to each other