account_research
ChatGPTResearch a company/account/organization using ZoomInfo market data with your CRM and conversation history. Handles both targeted queries ("What's their recent news?") and broad orientation ("What's going on with this account?"). USE THIS WHEN: You need information about a company, organization, or account (their business, news, financials, tech stack, leadership team, recent developments). DO NOT USE FOR: Individual person research (use Contact Research instead). ANSWERS QUESTIONS LIKE: • Company context: What does this company do? Who are their competitors? What's their tech stack? • Leadership: Who's in the C-suite? What's the background of their CTO? • Financials: What's their revenue? Have they raised funding recently? • Recent developments: Any recent news or executive changes? • Relationship status: What's our deal status? Who's on the buying committee? • Engagement overview: How engaged are we? Who are our key contacts? • Qualification: What's our MEDDIC status with this account? DOES NOT HANDLE: • Specific conversation queries ("What exactly did they say about X?") → Use Conversation Intelligence • Detailed engagement timelines or transcripts → Use Engagements • Structured data for export (revenue fields, headcount) → Use Enrich Companies FOR BEST RESULTS: Construct queries that frame the user's goal and describe what ideal context would look like. The more the query conveys about the situation and desired outcome, the better the response. General orientation: "User wants to understand what's going on with this account. Provide broad situational awareness — who they are, how the relationship stands, what's been happening, and anything notable I should know." Call prep: "User has a meeting tomorrow with their VP of Engineering. Provide context that helps them walk in prepared — understanding of the company, the person, the relationship history, and any active deals or recent developments." Deal review: "User is presenting this account at forecast. Provide context that supports a confident deal narrative — where the deal stands, who's involved, momentum indicators, and any risks." Competitive situation: "User is positioning against Salesforce at this account. Provide context that informs competitive strategy — what they use today, how they evaluate, and what we know about their buying process." Account planning: "User is building a strategic plan for this account. Provide context that supports territory strategy — the opportunity landscape, key relationships, coverage gaps, and growth signals." RETURNS: Market intelligence: company overview, leadership profiles, products/services, financials, competitors, technology stack, recent news. Relationship intelligence: CRM records, buying committee, key contacts, engagement summary, MEDDIC qualification. REQUIRES: zi_company_id (7-8 digit integer, e.g. '344589814'). Use Search Companies if you have a company name without the zoominfo company id. CREDITS: Each query consumes ZoomInfo AI credits. Credit consumption scales with the amount of context retrieved and the complexity of inference performed.
browse_audiences
ChatGPTBrowse GTM Studio audiences with basic metadata. Returns a paginated list of audiences. Filter results using createdByName (creator name), type (CONTACT or COMPANY record shape), or searchText (case-insensitive contains match on audience name). The id field in each result is the audienceId to use with getAudience. Working with identifiers: Always use id values returned by this tool when calling getAudience. Do not generate or guess identifier values.
browse_engagements
ChatGPTUse this tool to retrieve engagements (meetings and emails). Useful for tracking engagement history with external contacts. Supports filtering by date, zoominfoCompanyId and/or zoominfoContactId. The date range can be set with engagementDateStart and engagementDateEnd (defaulting to the last 7 days). This tool can only return results if the user's organization has set up integrations between ZoomInfo and at least one of its calendar, email, or meeting providers. Reach out to your account manager for more information. By default, results are returned in chronological order. To get the last few of an item, reverse the sort order with "-chronological". Does not consume data credits.
contact_research
ChatGPTResearch a specific person/contact using ZoomInfo market data with your CRM and conversation history. Handles both targeted queries ("What's their background?") and broad orientation ("Who is this person?"). USE THIS WHEN: You need information about an individual person (their role, background, contact details, career history). DO NOT USE FOR: Company/organization research (use Account Research instead). ANSWERS QUESTIONS LIKE: • Professional background: What's their career history? Where did they work before? • Current role: What do they do? What are their responsibilities? • Education & expertise: Where did they go to school? What's their expertise? • Company context: What company do they work for? What does that company do? • Contact info: What's their email? Phone number? LinkedIn? • Recent activities: Any recent news mentions or job changes? • Relationship status: How engaged are they? Are they in our CRM? • Buying involvement: Are they on a buying committee? What's their role in deals? DOES NOT HANDLE: • Specific conversation queries ("What exactly did they say about X?") → Use Conversation Intelligence • Detailed engagement timelines or transcripts → Use Engagements • Structured data for export (title, email, phone) → Use Enrich Contacts FOR BEST RESULTS: Construct queries that frame the user's goal and describe what ideal context would look like. The more the query conveys about the situation and desired outcome, the better the response. General orientation: "User wants to understand who this contact is. Provide broad situational awareness — their background, current role, company, and anything notable I should know." Meeting prep: "User has a meeting tomorrow with this person. Provide context that helps them walk in prepared — understanding of their role, background, recent activities, and our relationship history." Outreach planning: "User is planning to reach out to this contact. Provide context that informs a personalized approach — who they are, what they care about, how they engage, and any warm introduction paths." Champion building: "User wants to build this person into a champion. Provide context that supports relationship development — their influence, interests, engagement patterns, and what motivates them." Stakeholder mapping: "User is mapping stakeholders for a deal. Provide context that clarifies this person's role — their influence, relationships, decision authority, and how they fit into the buying process." RETURNS: Contact intelligence: professional background, current role, education, contact information, company context, recent activities. Relationship intelligence: CRM records, engagement history, buying involvement, communication patterns. REQUIRES: zoominfoContactId (7-9 digit integer, e.g. '123456789'). Use Search Contacts if you have a contact name or email without the zoominfo contact id. CREDITS: Each query consumes ZoomInfo AI credits. Credit consumption scales with the amount of context retrieved and the complexity of inference performed.
enrich_companies
ChatGPTEnrich 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 CREDITS: This tool consumes ZoomInfo Bulk Credits. Once a company is enriched using this tool or on ZoomInfo's platform, subsequent enrichments of the company will not consume credits for one year from the first enrichment.
enrich_contacts
ChatGPTEnrich contacts data with verified business information from ZoomInfo's database. All data returned by this tool is strictly business-related — including corporate email addresses, business phone numbers, job details, company data, and professional network information. This tool does not return personal contact information such as private phone numbers, personal email addresses, or non-professional data of any kind. 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 CREDITS: This tool consumes ZoomInfo Bulk Credits. Once a contact is enriched using this tool or on ZoomInfo's platform, subsequent enrichments of the contact will not consume credits for one year from the first enrichment.
enrich_intent
ChatGPTFetch Intent Signals for a specific company, enriching that company's details with relevant buyer intent data. Required: Each request must include at least one company identifier (companyId, companyName, or companyWebsite) and at least 1 and up to 50 intent topics. Key Terminology: • Topic - A business subject or technology area mapped to a curated collection of keywords and search terms. When companies research content containing these keywords, it generates intent signals that indicate their buying interest • Signal Score - Indicates the level of a company's interest in a topic (60-100) based on how recent content consumption compares to an historical baseline • Audience Strength - Indicates the size of the group at the company that is conducting the research (A=largest audience, E=smallest) 🔍 Recommended Workflow - Using Lookup for Accurate Results: For intent enrichment with specific topics, you should: 1. Call lookup with fields: [{ fieldName: "intent-topics" }] to get standardized topic values 2. Call enrich_intent with exact topic names from lookup results and company identifier Example: "Find ZoomInfo's intent signals for Cloud Applications and Java": `` 1. lookup(fields: [{ fieldName: "intent-topics", fuzzyMatch: "cloud" }, { fieldName: "intent-topics", fuzzyMatch: "java" }]) → Returns topics: ["Cloud Applications", "Java", ...] 2. enrich_intent(companyName: "ZoomInfo", topics: ["Cloud Applications", "Java"], signalScoreMin: 70) `` Use Enrich Intent to find Intent Signals for a specific company. To search Intent Signals across all ZoomInfo companies, use the Search Intent endpoint. CREDITS: This endpoint charges a single credit for the company being enriched. Each Intent Signal returned in the results is counted as a Record, and a successful response counts as a Request credit.
enrich_news
ChatGPTFetch News articles for a specific company, enriching that company's details with relevant news coverage. Required: Each request must include company identifier (zoominfoCompanyId). For news enrichment filtered by specific categories, pass one or more NewsCategoryEnum values to the categories field. Example: "Find ZoomInfo's recent product news": `` enrich_news(zoominfoCompanyId: 123123123, categories: ["PRODUCT"], publishingDateStart: "2026-01-01") `` Credit Usage: This endpoint charges a single credit for the enriched company. Each News article returned in the results is counted as a Record, and a successful response counts as a Request credit. For example, a successful response that returns 10 news articles, 1 credit will be charged if applicable, 10 Records will be counted, and 1 Request will be counted.
enrich_scoops
ChatGPTEnrich the specified company with relevant Scoops. A company identifier must be provided (zoominfoCompanyIds, companyName, or companyWebsites); all other criteria are optional. KEY PARAMETERS: • zoominfoCompanyIds - ZoomInfo unique identifiers for the company (most accurate) • companyName - Company name • companyWebsites - Company website URLs in https://www.example.com format OPTIONAL FILTERS: • scoopIds - ZoomInfo unique identifiers for Scoops • scoopTypes - Retrieve scoops based on type (e.g. Earnings, Award, and Partnership). • scoopTopics - Retrieve scoops based on topic (e.g. integration, consolidation and compliance). Accepts a list of IDs from the lookup tool • department - Retrieve scoops based on department (Information Technology, Finance, Human Resources and so on). • description - Search for scoops based on description. Accepts a space-separated list of individual words • publishedStartDate - Starting date to search for scoops based on publication date (YYYY-MM-DD format) • publishedEndDate - Ending date to search for scoops based on publication date (YYYY-MM-DD format) • updatedSinceCreation - Default is false. Setting true will only return scoops that have been updated since publishedStartDate USE CASES: Scoop data can be used to: - Identify timely business events and trigger outreach based on company news - Track company changes such as leadership moves, expansions, and partnerships - Prioritize accounts based on recent activity signals - Personalize outreach with relevant, up-to-date company context CREDIT USAGE: With reference to ZoomInfo's Credit Usage and Limits, a credit will be counted for the enriched company. Each Scoop returned in the results counts towards the customer's Record limit and a successful response will count towards the Request limit.
find_similar_companies
ChatGPTUse 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. CREDITS: Free to use
find_similar_contacts
ChatGPTUse 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. Important: This tool is designed for legitimate business research. Users are responsible for compliance with applicable privacy laws (GDPR, CCPA, CAN-SPAM). 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. 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. 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 identify appropriate business contacts for research and strategic planning purposes. 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. CREDITS: Free to use
get_audience
ChatGPTFetch detailed data for a specific GTM Studio audience, including column definitions and an optional row preview. audienceId must be obtained from browseAudiences — do not generate or guess it. Set previewRows to true to include row preview data; by default, 5 rows are returned unless previewRowLimit is provided. Use previewRowLimit (max 25) to control row preview size and previewColumnIds to scope preview columns.
get_gtm_context
ChatGPTReturns the authenticated user's GTM context: their role, what their organization offers (products and services), ideal customer profiles, buyer personas, competitors, and strategic priorities. No parameters required — identity is resolved from authentication. This is read-only; the user needs admin permissions to create, update, delete or archive configuration. No ZoomInfo credits consumed. This context shapes how other ZoomInfo tools deliver value. Search, enrichment, and prospecting results are more relevant when filtered and scored against the user's ideal customer profiles, buyer personas, and competitive landscape. Without it, those tools return unqualified results that require manual assessment. Compressed by default (~1,000 tokens). Use detailed=true for expanded profiles.
get_recommended_contacts
ChatGPTUse 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 identify appropriate business contacts for research and strategic planning purposes. Compliance Note: Search results are intended for professional research. Ensure all use aligns with your organization's data governance policies. 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. 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 identify appropriate business contacts for research and strategic planning purposes. 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? CREDITS: Free to use
lookup
ChatGPTLookup reference data - This tool provides standardized parameter values for use in search operations. Using lookup helps ensure accurate search results. Note: Use the `id` field, not `attributes.name` • Lookup results contain both id and attributes.name • Use the id value in search parameters • The attributes.name is for human readability only • Example: Use id: "12345" not attributes.name: "Vice President" Recommended Workflow: 1. Call lookup to get exact field values 2. Extract the id field from each result 3. Use the id values in search_contacts or search_companies 4. This helps prevent search failures and ensures accurate results Single field lookup: `` lookup(fields: [{ fieldName: "management-levels" }]) → Get VP options, use result.id ` **Multiple fields lookup:** ` lookup(fields: [{ fieldName: "management-levels" }, { fieldName: "metro-regions" }, { fieldName: "industries" }, { fieldName: "employee-count" }]) → Get all options, use result.id values ` **Fuzzy match filtering:** ` lookup(fields: [{ fieldName: "tech-vendors", fuzzyMatch: "hubspot" }]) → Returns vendors matching "hubspot", use result.id (e.g., "HubSpot, Inc") lookup(fields: [{ fieldName: "industries", fuzzyMatch: "software" }]) → Returns industries containing "software", use result.id ` • Use fuzzyMatch within each field object 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" **Note**: For batch lookups with multiple fields, pass each as a separate object in the fields array. - Per-field fuzzyMatch is set on each field object individually (e.g., [{ fieldName: "metro-regions", fuzzyMatch: "boston" }, { fieldName: "industries" }] filters field 1, not field 2) - Omit fuzzyMatch or set it to null to skip filtering for a specific field **Example: VP-level tech contacts in San Francisco (100-500 employees):** ` Step 1: lookup(fields: [{ fieldName: "management-levels" }, { fieldName: "metro-regions", fuzzyMatch: "san francisco" }, { fieldName: "industries", fuzzyMatch: "software" }, { fieldName: "employee-count" }]) → Get all options at once Step 2: Extract id values from results Step 3: search_contacts(managementLevel: "<id>", metroRegion: "<id>", industryCodes: "<id>", employeeCount: "<id>") ` **Example: Companies using specific technology (e.g., "Companies in Boston using HubSpot"):** ` Step 1: lookup(fields: [{ fieldName: "tech-vendors", fuzzyMatch: "hubspot" }]) → Get vendor id Step 2: lookup(fields: [{ fieldName: "tech-products" }], vendor: "<vendor_id>") → Get HubSpot product ids Step 3: lookup(fields: [{ fieldName: "metro-regions", fuzzyMatch: "boston" }]) → Get metro id Step 4: search_companies(techAttributeTagList: "<product_ids>", metroRegion: "<metro_id>") ` **Note:** For technology searches, lookup tech-vendors (with fuzzyMatch), then lookup tech-products (with vendor parameter), and use the id values. **Common lookup fields:** • **management-levels** - VP, Director, C-Level classifications (for seniority searches) • **metro-regions** - Exact metropolitan area names (for location searches) • **industries** - Industry classifications (for sector searches) • **employee-count** - Company size ranges (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, use before tech-products lookup) • **tech-products** - Technology product IDs (use vendor parameter with exact vendor name from tech-vendors lookup) • **tech-categories** - Technology categories for broader tech stack searches **Note:** Search tools may fail if you use incorrect field values. Consider using lookup and the id` field for accuracy. Note: When looking up hashtags and intent-topics, this tool will return a maximum of 100 records. If more than 100 records exist, the response will include a truncation notice indicating that results were limited. CREDITS: Free to use
search_companies
ChatGPTSearch 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. 🔍 Recommended Workflow - Using Lookup for Accurate Results: For company searches with specific criteria, you can: 1. Call lookup with appropriate fieldNames to get standardized values 2. Call search_companies with exact values from lookup results Example for "tech companies in San Francisco with 100–500 employees": `` 1. lookup(fields: [{ fieldName: "metro-regions", fuzzyMatch: "san francisco" }]) → Returns: "CA - San Francisco" 2. lookup(fields: [{ fieldName: "industries", fuzzyMatch: "software" }]) → Returns: "Computer Software", "Information Technology Services" 3. lookup(fields: [{ fieldName: "employee-count" }]) → Returns: "101-250", "251-500" 4. search_companies(metroRegion: "CA - San Francisco", industryCodes: "Computer Software", employeeCount: "101-250,251-500") ` **Example for "Companies in Boston using HubSpot":** ` 1. lookup(fields: [{ fieldName: "tech-vendors", fuzzyMatch: "hubspot" }]) → Returns vendor: "HubSpot, Inc" 2. lookup(fields: [{ fieldName: "tech-products" }], vendor: "HubSpot, Inc") → Returns HubSpot product IDs 3. lookup(fields: [{ fieldName: "metro-regions", fuzzyMatch: "boston" }]) → Returns: "MA - Boston" 4. search_companies(techAttributeTagList: "<product_ids>", metroRegion: "MA - Boston") `` Note: For technology stack searches, lookup tech-vendors first, then lookup tech-products with vendor parameter Note: Using exact values returned by lookup tools helps ensure accurate search results. Guessing field values will cause search failures. Consider using 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) CREDITS: Free to use
search_contacts
ChatGPTSearch 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. 🔍 Recommended Workflow - Using Lookup for Accurate Results: For the query "Find all VP-level contacts at tech companies in San Francisco with 100–500 employees", you can: 1. Call lookup with fields: [{ fieldName: "management-levels" }] to get VP-level options 2. Call lookup with fields: [{ fieldName: "metro-regions", fuzzyMatch: "san francisco" }] to get San Francisco metro area name 3. Call lookup with fields: [{ fieldName: "industries", fuzzyMatch: "software" }] to get technology industry codes 4. Call lookup with fields: [{ fieldName: "employee-count" }] to get 100-500 employee range 5. Call search_contacts with the exact values from lookup results Note: Using exact values returned by lookup tools helps ensure accurate search results. Guessing field values will cause search failures. Example Workflow: `` 1. lookup(fields: [{ fieldName: "management-levels" }]) → Returns: "Vice President", "Senior Vice President", etc. 2. lookup(fields: [{ fieldName: "metro-regions", fuzzyMatch: "san francisco" }]) → Returns: "CA - San Francisco" 3. lookup(fields: [{ fieldName: "industries", fuzzyMatch: "software" }]) → Returns: "Computer Software", "Information Technology Services", etc. 4. lookup(fields: [{ fieldName: "employee-count" }]) → Returns: "101-250", "251-500" 5. search_contacts(managementLevel: "Vice President", metroRegion: "CA - San Francisco", industryCodes: "Computer Software", employeeCount: "101-250,251-500") `` Consider using 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) CREDITS: Free to use
search_intent
ChatGPTSearch for Intent Signals across ZoomInfo's comprehensive database. Intent Signals indicate recent content consumption and buying interest by companies for specific topics. Required: Every request must include at least 1 and up to 50 intent topics. Key Terminology: • Topic - A business subject or technology area mapped to a curated collection of keywords and search terms. When companies research content containing these keywords, it generates intent signals that indicate their buying interest • Signal Score - Indicates the level of a company's interest in a topic (60-100) based on how recent content consumption compares to an historical baseline • Audience Strength - Indicates the size of the group at the company that is conducting the research (A=largest audience, E=smallest) 🔍 Recommended Workflow - Using Lookup for Accurate Results: For intent searches with specific topics, you should: 1. Call lookup with fields: [{ fieldName: "intent-topics" }] to get standardized topic values 2. Call search_intent with exact topic names from lookup results Example: "Find companies interested in Cloud Applications and Java": `` 1. lookup(fields: [{ fieldName: "intent-topics", fuzzyMatch: "cloud" }, { fieldName: "intent-topics", fuzzyMatch: "java" }]) → Returns topics: ["Cloud Applications", "Java", ...] 2. search_intent(topics: ["Cloud Applications", "Java"], signalScoreMin: 70, audienceStrengthMin: "B") ` **Example: "Companies showing high intent for Mobile Apps in the last 30 days":** ` 1. lookup(fields: [{ fieldName: "intent-topics", fuzzyMatch: "mobile" }]) → Returns topics: ["Mobile Apps", "Mobile / Wireless", ...] 2. search_intent(topics: ["Mobile Apps"], signalScoreMin: 80, signalStartDate: "2024-01-01", signalEndDate: "2024-01-31") `` Note: Using exact topic names returned by lookup ensures accurate search results. The lookup response includes both a topics array (topic names) and topicDetails array (with additional metadata like category, department, description). Use the topic names from either array in your search_intent request. Use Search Intent to find Intent Signals across all ZoomInfo companies. To find Intent Signals for a specific company, use the Enrich Intent endpoint. CREDITS: Free to use
search_scoops
ChatGPTSearch for Scoops across ZoomInfo's comprehensive database. Scoops are real-time business intelligence signals that provide timely insights into significant events and changes occurring at companies. SCOOP FILTERS: • scoopIds - ZoomInfo unique identifiers for Scoops • scoopTypes - Retrieve scoops based on type (e.g. Earnings, Award, and Partnership). • scoopTopics - Retrieve scoops based on topic (e.g. integration, consolidation and compliance). Accepts a list of IDs from the lookup tool • department - Retrieve scoops based on department (IT, finance, HR and so on). • description - Search for scoops based on description. Accepts a space-separated list of individual words • publishedStartDate - Starting date to search for scoops based on publication date (YYYY-MM-DD format) • publishedEndDate - Ending date to search for scoops based on publication date (YYYY-MM-DD format) • updatedSinceCreation - Setting true will only return scoops that have been updated since publishedStartDate COMPANY FILTERS: • companyTicker, companyDescription, companyTypes - Identify or describe specific companies • industryCodes, metroRegions, employeeCount, revenue - Filter by company attributes • techAttributeTagList - Filter by technology stack (use Lookup tech-products for valid values) CONTACT FILTERS: • jobTitle, managementLevel, department, jobFunction - Filter by contact role • firstName, lastName, emailAddress - Filter by specific contact SORT OPTIONS: • scoopId, originalPublishedDate (default: -originalPublishedDate), description, link, linkText • Prefix with '-' for descending order USE CASES: • Find companies with recent leadership changes or expansions • Identify companies that recently received funding or made acquisitions • Track industry-specific business events for timely outreach • Discover companies showing signals of buying intent through business events RECOMMENDED WORKFLOW: 1. Use Lookup with fieldName: "scoop-topics" to get valid filter IDs 2. Call search_scoops with the desired filters 3. Use Enrich Scoops for detailed scoop data on a specific company CREDIT USAGE: This endpoint does not consume any Credits. Each Scoop returned counts towards the customer's Record Limit and a successful response counts towards the Request Limit.
submit_feedback
ChatGPTUse this tool to submit feedback on ZoomInfo MCPs or other feedback for ZoomInfo. Required: - Provide feedback content. - Provide a category for the feedback. Select the most appropriate value: DATA_QUALITY, FEATURE_REQUEST, ACCESS_ENTITLEMENT_ISSUE, or OTHER.
update_gtm_context
ChatGPTUpdate your organization's GTM configuration. Can synthesize unstructured content into structured records, or make direct changes to existing configuration. WHAT IT CREATES/UPDATES: • Company Overview (name, elevator pitch, description, strategic priorities, custom fields) • Offerings (products/services, value props, pain points, proof points, custom fields) • Buyer Personas (roles, objectives, pain points, priorities, custom fields) • Ideal Customer Profiles (segments, firmographics, technographics, custom fields) • Competitors (products, strengths, weaknesses, win/loss reasons, custom fields) ACTIONS: The agent can create, read, update and delete configuration. Offerings, Buyer Personas, Ideal Customer Profiles and Competitors can be archived and un-archived. CUSTOM FIELDS: The agent can create and populate custom fields on any object type. When you provide information that doesn't fit standard fields, the agent will create appropriate custom fields to preserve that context. For example: "integration timeline", "decision maker", "budget cycle", or domain-specific attributes unique to your sales process. MULTI-OBJECT OPERATIONS: A single call can affect multiple object types. The agent deduplicates against existing configuration and enriches existing records where appropriate. FOR BEST RESULTS: The query frames your intent, while sourceMaterial provides substance to analyze. When sourceMaterial is provided, richer content produces higher-quality records. When omitted, the agent operates on existing configuration directly (archive, rename, simple updates). CONTEXT LIMITS: sourceMaterial should not exceed ~2,000 words per call. For larger documents, break into multiple calls with scoped queries. CREDITS: Each query consumes ZoomInfo AI credits. Credit consumption scales with the amount of content analyzed and the complexity of operations performed. ACCESS: User must be an admin in the ZoomInfo system.
enrich_company
Claudeenrich_contact
Claudelookup
Claudesearch_companies
Claudesearch_contacts
Claude
