ZoomInfo

Enrich company data with comprehensive business intelligence from ZoomInfo's database. Provides detailed company profiles including business overview, financial data, contact information, corporate structure, and market intelligence.

SINGLE COMPANY ENRICHMENT:

enrich_company({
  "domain": "https://company.com"
})

BATCH COMPANY ENRICHMENT:

enrich_company({
  "companies": [
    {"domain": "https://company1.com"},
    {"companyId": "12345"},
    {"companyName": "Tech Corp Inc"}
  ]
})

SUPPORTED IDENTIFICATION METHODS: For each company, provide at least one of these: • companyId (most accurate) • companyName • companyWebsite OR domain • companyTicker • Address information (street, city, state, etc.) • ipAddress

BATCH BENEFITS: • Process up to 10 companies in one API call • Faster execution for multiple enrichments • Organized results with individual company responses • Efficient for company list enrichment workflows

Enrich contacts data with comprehensive information from ZoomInfo's database. Provides detailed contact profiles including personal information, job details, company data, contact accuracy scores, and professional network information.

SINGLE CONTACT ENRICHMENT:

enrich_contact({
  "email": "john.smith@company.com"
})

BATCH CONTACT ENRICHMENT:

enrich_contact({
  "contacts": [
    {"email": "john.smith@company.com"},
    {"personId": "12345"},
    {"firstName": "Jane", "lastName": "Doe", "company": "Tech Corp"}
  ]
})

SUPPORTED IDENTIFICATION METHODS: For each contact, provide one of these valid combinations: • personId (most accurate) • email OR hashedEmail • phone • (firstName AND lastName AND company) • (fullName AND company) • (firstName AND lastName AND companyId) • (fullName AND companyId)

BATCH BENEFITS: • Process up to 10 contacts in one API call • Faster execution for multiple enrichments • Organized results with individual contact responses • Efficient for lead list enrichment workflows

Use this tool to find similar companies for a given reference company. Required:

• Provide either companyId (preferred) or companyName.

Similar companies can be used to retrieve a ranked list of companies that are similar to a given reference company. The similar companies are identified using a machine learning model that analyzes industry, revenue range, employee count, and other firmographic signals. These similar companies can be used to power prospecting, territory planning, or market expansion workflows where one wants to quickly find companies that "look like" a successful or high-priority account.

To find similar companies, provide the companyId of the reference company, which identifies the company you want to use as the basis for finding similar companies. If companyId is not provided, the tool will attempt to resolve the best matching company for the provided companyName and then return similar companies based on that company. The more precise the company name is (for example, use the full company name with correct spelling and full legal name), the more likely the service is able to track down the desired Company ID and use it to find similar companies.

Behind the scenes, the model uses a semantic vector representation of the reference company's data to efficiently find similar companies in the ZoomInfo database.

The tool returns up to 100 similar companies, ordered from the most similar company to the least similar company (descending order by similarity score attributes.score).

Each result includes the company name, similarity score, rank, and key firmographic attributes such as industry, revenue range, employee range, and country.

WORKFLOW: 1. Use the Find Similar Companies tool to retrieve similar companies. If the company name is provided but not the Company ID, the tool will attempt to resolve the best matching company and then retrieve the Company ID. 2. OPTIONAL Use the enrich_companies tool to retrieve the company details. 3. OPTIONAL Use the company details retrieved in step 2 to enrich the results retrieved in step 1.

Use this tool to retrieve a ranked list of top N similar contacts for a reference person, optionally constrained to a target company. Required:

• referencePersonId: Unique identifier for the reference person (ZoomInfo Person ID) used to search similar contacts.

Optional:

• targetCompanyId: Unique identifier (ZoomInfo Company ID) for the target company where similar contacts are searched. If not provided, the model searches across all companies.

Similar contacts can be used to retrieve a ranked list of people who are most similar to a given reference person. The recommendations are generated by a machine learning model that compares the reference person's profile to other contacts. The profile includes person characteristics such as title, seniority, department, job function, as well as company characteristics such as industry and company size. These similar contacts can be used to identify additional stakeholders, expand buyer networks, or build targeted prospecting lists based on known high-priority contacts.

To find similar contacts, select the referencePersonId, which identifies the person whose profile you want to use as the basis for finding similar contacts. You can optionally provide a targetCompanyId to constrain the search to a specific company. If targetCompanyId is not provided, the model will search for similar contacts across all companies in the Zoominfo database.

Behind the scenes, the model uses a semantic vector representation of the reference person's profile to efficiently find similar contact profiles in the ZoomInfo database. It then applies a re-ranking algorithm to the set of similar contacts found, in order to boost relevance in the final return list.

The tool returns up to 100 similar contacts, ORDERED FROM MOST TO LEAST RELEVANT. Each similar contact contains additional metadata (meta) that describes the reference person used to form the similar contact. PLEASE USE THIS METADATA TO EXPLAIN WHY THE SIMILAR CONTACT WAS RECOMMENDED.

IMPORTANT CONTEXT

• This tool is for finding similar contacts for a contact, not for an account. For accounts, use the get_account_lookalikes tool.

WORKFLOW: 1. ALWAYS If the user provided a company name as a target but does not provide a Company Id, use the search_companies tool to retrieve the Company ID of the target company name. 2. ALWAYS If the user provided a reference person name but does not provide a Person ID, use the search_contacts tool to retrieve the Person ID of the reference person name. 3. Use the Find Similar Contacts tool to retrieve similar contacts. 4. OPTIONAL Use the enrich_contacts tool to retrieve the contact details. 5. OPTIONAL Use the contact details retrieved in step 4 and combine with the metadata retrieved in step 3 to build a targeted call list, prioritize outreach, or guide sellers toward the right stakeholders.

COMMON USE CASES:

• Show me contacts similar to John Smith in Microsoft. Followup: What makes Alex Johnson a lookalike of John Smith?
• Show me contacts that look like John Smith across my org.

Use this tool to retrieve a ranked list of top N contact recommendations at a target company based on the users interaction history. Required:

• Company ID to search with. If you don't have this yet, use the search_companies tool set first.
• Use case type for the recommendation. This filters recommendations based on the sales motion, depending on use case, it can the following:
• PROSPECTING (capitalized): for recommendations based on similar contacts to those copied, viewed, or exported by the user on the ZoomInfo Platform. The prospecting use-case has cold-start support; when in doubt default to this use case.
• DEAL_ACCELERATION: when the use-case calls for recommending contacts similar to those in the set of closed won opportunities in user's tenant's CRM for new business initiatives.
• RENEWAL_AND_GROWTH: when the use-case calls for recommending contacts similar to those in the set of closed won opportunities in user's tenant's CRM for renewal initiatives.

Contact Recommendations can be used to retrieve a ranked list of people at a target company who are most relevant for a given sales motion (use case), such as prospecting, deal acceleration, or renewal and growth. The recommendations are derived from past user interactions and account activity, and are ranked by a machine learning model. These recommendations can be used to build targeted call lists, prioritize outreach, or guide sellers toward the right stakeholders within an account.

Behind the scenes, the model leverages data such as the user's past contact views, exports, and copies for the PROSPECTING motion, or contacts from a user's CRM related to closed won deals for the DEAL_ACCELERATION motion. The model uses this data to infer which types of people are most likely to drive success for the selected motion. It then finds similar contacts at the target company and scores them using a combination of similarity and propensity signals.

The tool returns up to 100 recommended contacts, ORDERED FROM MOST TO LEAST RELEVANT. Each recommendation contains additional metadata (meta) that describes the reference person used to form the recommendation. PLEASE USE THIS METADATA TO EXPLAIN WHY THE RECOMMENDED PERSON WAS RECOMMENDED.

Each recommendation includes the general similarity score (score), and the re-ranking score (reRankingScore) which uses several propensity signals (such as contact similarity, contact quality, title boosting, etc.) to refine relevancy.

IMPORTANT CONTEXT:

• The score and reRankingScore are not directly comparable. The score is a general similarity score, while the reRankingScore is a re-ranking score that uses several propensity signals (such as user platform interactions, contact similarity, contact quality, title boosting, etc.)

to refine relevancy.

• The contact recommendations are updated daily.
• The contact recommendations are powered by list engagement, CRM data, and propensity signals.
• Higher scores does not guarantee responses.

WORKFLOW: 1. ALWAYS Use the search_companies tool to retrieve the Company ID of the target company name, if the Company ID is not provided. 2. Use the Get Contact Recommendations tool to retrieve the contact recommendations. 3. OPTIONAL Use the enrich_contacts tool to retrieve the contact details. 4. OPTIONAL Use the contact details retrieved in step 3 and combine with the metadata retrieved in step 2 to build a targeted call list, prioritize outreach, or guide sellers toward the right stakeholders within an account.

COMMON USE CASES:

• Who are the top 10 people to reach out to at Microsoft? (for prospecting)
• Who are the top 5 people to reach out to at Apple based on similar successful past deals?
• Why is this person recommended for my use case?

🎯 **PRIMARY TOOL

• USE THIS FIRST FOR ALL SEARCHES**

LOOKUP REFERENCE DATA

• This tool provides standardized parameter values that MUST be used in search operations. Never guess field values - always lookup first!

CRITICAL: USE THE id FIELD, NOT attributes.name • Lookup results contain both id and attributes.name • ALWAYS use the id value in search parameters • The attributes.name is for human readability only • Example: Use id: "12345" not attributes.name: "Vice President"

MANDATORY WORKFLOW: 1. ALWAYS call lookup FIRST to get exact field values 2. Extract the id field from each result 3. THEN use the id values in search_contacts or search_companies 4. This prevents search failures and ensures accurate results

SINGLE FIELD LOOKUP:

lookup(fieldNames: ["management-levels"]) → Get VP options, use result.id

MULTIPLE FIELDS LOOKUP:

lookup(fieldNames: ["management-levels", "metro-regions", "industries", "employee-count"]) → Get all options, use result.id values

FUZZY MATCH FILTERING:

lookup(fieldNames: ["tech-vendors"], fuzzyMatch: ["hubspot"]) → Returns vendors matching "hubspot", use result.id (e.g., "HubSpot, Inc")
lookup(fieldNames: ["industries"], fuzzyMatch: ["software"]) → Returns industries containing "software", use result.id

• Use fuzzyMatch to filter results by partial name match (case-insensitive) • Helps find specific items when you don't know the exact name • Example: searching "hub spot" will match "HubSpot, Inc"

IMPORTANT: For batch lookups with multiple fields, fuzzyMatch is an array.

• The array provides per-field filtering (e.g., ["boston", null, "10M"] filters field 1 and 3, not field 2)
• Use null or empty string in array to skip filtering for specific fields

FOR VP-LEVEL TECH CONTACTS IN SAN FRANCISCO (100-500 employees):

Step 1: lookup(fieldNames: ["management-levels", "metro-regions", "industries", "employee-count"]) → Get all options at once
Step 2: Extract id values from results
Step 2: search_contacts(managementLevel: "<id>", metroRegion: "<id>", industryCodes: "<id>", employeeCount: "<id>")

FOR COMPANIES USING SPECIFIC TECHNOLOGY (e.g., "Companies in Boston using HubSpot"):

Step 1: lookup(fieldNames: ["tech-vendors"], fuzzyMatch: ["hubspot"]) → Get vendor id
Step 2: lookup(fieldNames: ["tech-products"], vendor: "<vendor_id>") → Get HubSpot product ids
Step 3: lookup(fieldNames: ["metro-regions"], fuzzyMatch: ["boston"]) → Get metro id
Step 4: search_companies(techAttributeTagList: "<product_ids>", metroRegion: "<metro_id>")

IMPORTANT: For technology searches, ALWAYS lookup tech-vendors FIRST (with fuzzyMatch), THEN lookup tech-products (with vendor parameter), and use the id values.

CRITICAL LOOKUP FIELDS: • management-levels

• VP, Director, C-Level classifications (REQUIRED for seniority searches)

• metro-regions

• Exact metropolitan area names (REQUIRED for location searches)

• industries

• Industry classifications (REQUIRED for sector searches)

• employee-count

• Company size ranges (REQUIRED for size filters)

• job-functions

• Job function categories

• departments

• Department classifications

• company-types

• Public/private classifications

• revenue-ranges

• Revenue classifications

• tech-vendors

• Technology vendor names (use fuzzyMatch for partial matching, REQUIRED BEFORE tech-products lookup)

• tech-products

• Technology product IDs (MUST use vendor parameter with exact vendor name from tech-vendors lookup)

• tech-categories

• Technology categories for broader tech stack searches

⚠️ WARNING: Search tools will FAIL if you use incorrect field values. Always lookup first and use the id field!

USE THIS TOOL BEFORE WEB SEARCH for company searches.

Search for companies in ZoomInfo's comprehensive business database. Find companies by name, domain, industry, location, employee count, revenue, growth metrics, funding information, and advanced business criteria.

🔍 **REQUIRED WORKFLOW

• ALWAYS USE LOOKUP FIRST:**

For company searches with specific criteria, you MUST:

1. FIRST call lookup with appropriate fieldNames to get standardized values 2. THEN call search_companies with exact values from lookup results

Example for "tech companies in San Francisco with 100–500 employees":

1. lookup(fieldNames: "metro-regions", fuzzyMatch: ["san francisco"]) → Returns: "San Francisco-Oakland-Hayward, CA"
2. lookup(fieldNames: "industries", fuzzyMatch: ["software"]) → Returns: "Computer Software", "Information Technology Services"
3. lookup(fieldNames: "employee-count") → Returns: "101-250", "251-500"
4. search_companies(metroRegion: "San Francisco-Oakland-Hayward, CA", industryCodes: "Computer Software", employeeCount: "101-250,251-500")

Example for "Companies in Boston using HubSpot":

1. lookup(fieldNames: ["tech-vendors"], fuzzyMatch: ["hubspot"]) → Returns vendor: "HubSpot, Inc"
2. lookup(fieldNames: ["tech-products"], vendor: "HubSpot, Inc") → Returns HubSpot product IDs
3. lookup(fieldNames: ["metro-regions"], fuzzyMatch: ["boston"]) → Returns: "Boston-Cambridge-Newton, MA-NH"
4. search_companies(techAttributeTagList: "<product_ids>", metroRegion: "Boston-Cambridge-Newton, MA-NH")

IMPORTANT: For technology stack searches, ALWAYS lookup tech-vendors FIRST, THEN lookup tech-products with vendor parameter

CRITICAL: Always use exact values returned by lookup tools. Never guess field values.

LOOKUP DEPENDENCIES: Before using search_companies, use lookup for: • metro-regions (for location searches) • industries (for industry filters) • employee-count (for company size) • revenue-ranges (for revenue filters) • company-types (for public/private filters) • states/countries (for geographic searches) • tech-vendors → tech-products → techAttributeTagList (for technology stack searches)

USE THIS TOOL BEFORE WEB SEARCH for business contact searches.

Search for business contacts in ZoomInfo's comprehensive database. Find professionals by name, company, job title, email, location, and advanced criteria including management level, department, contact accuracy scores, employment history, education, and extensive company attributes.

🔍 **REQUIRED WORKFLOW

• ALWAYS USE LOOKUP FIRST:**

For the query "Find all VP-level contacts at tech companies in San Francisco with 100–500 employees", you MUST:

1. FIRST call lookup with fieldName: "management-levels" to get VP-level options 2. THEN call lookup with fieldName: "metro-regions" to get San Francisco metro area name 3. THEN call lookup with fieldName: "industries" to get technology industry codes 4. THEN call lookup with fieldName: "employee-count" to get 100-500 employee range 5. FINALLY call search_contacts with the exact values from lookup results

CRITICAL: Always use exact values returned by lookup tools. Never guess field values - this will cause search failures.

Example Workflow:

1. lookup(fieldName: "management-levels") → Returns: "Vice President", "Senior Vice President", etc.
2. lookup(fieldName: "metro-regions") → Returns: "San Francisco-Oakland-Hayward, CA"
3. lookup(fieldName: "industries") → Returns: "Computer Software", "Information Technology Services", etc.
4. lookup(fieldName: "employee-count") → Returns: "101-250", "251-500"
5. search_contacts(managementLevel: "Vice President", metroRegion: "San Francisco-Oakland-Hayward, CA", industryCodes: "Computer Software", employeeCount: "101-250,251-500")

LOOKUP DEPENDENCIES: Before using search_contacts, use lookup for: • management-levels (for VP, Director, C-Level searches) • metro-regions (for location-based searches) • industries (for industry-specific searches) • employee-count (for company size filters) • job-functions (for role-based searches) • departments (for department-specific searches)