- Brand
- ZoomInfo
- Category
- Sales & CRM
- Primary Subcategory
- B2B Prospecting & Contact Data
Integration details
Description
ZoomInfo — B2B company and professional intelligence. Use for prospecting, finding people at companies, current and past employment, job changes, org charts, executives, verified business contact information, company research, intent, Scoops, and GTM signals.
- Integration type
- Plugin
- Verification status
- Not applicable
- Platform
- ChatGPT
- Primary Subcategory
- B2B Prospecting & Contact Data
- Secondary Subcategories
- None listed
- Brand
- ZoomInfo
- Access
- Account required
- First tracked
- 2026-09-01
- Tool count
- 20
- Market
- US
The Primary Subcategory used for this profile’s headline score.
Other Subcategories where the Integration is listed.
Agent Discoverability
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Competing in ChatGPT B2B Prospecting & Contact Data
View Category20 tools agents can invoke
Research 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 • Raw/paginated signal export (Intent, News, Scoop) for downstream systems or bulk records → Use Enrich Company Signals 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. Insight signals: recent trigger events — funding, M&A, earnings, layoffs, partnerships, IPO/divestiture, product launches, leadership and buying-committee changes, hiring anomalies, website visits, and intent spikes (directional indicators). 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.
account_research
Browse 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). Working with identifiers: This tool returns audience IDs. Use each result's id as audienceId for getAudience, audienceAnalysis, and audience write tools.
browse_audiences
Use 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). The window between engagementDateStart and engagementDateEnd must be no more than 90 days, with engagementDateStart before engagementDateEnd. 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.
browse_engagements
Research 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.
contact_research
Answers natural-language questions about calls, meetings, and emails, scoped to a single engagement, an account, or a contact. Queries should carry the user's detailed intent and relevant conversation context, including any specified date range of interest (default lookback: 365 days). Requires a meeting or email provider configured in ZoomInfo. USE THIS WHEN: - You need to analyze conversations for objections, commitments, sentiment, next steps, or action items. - You need to surface what was actually said in calls, meetings, or emails — requirements, promises, or concerns. - NOT for CRM fields, firmographics, or market data. ANSWERS QUESTIONS LIKE: Engagement: "What were the customer's main objections on this call?" · "What was agreed that a follow-up should reference?" Account: "What concerns has this account raised recently? What did we promise them?" Contact: "What has this person told us they care about?" SCOPING — pass exactly one ID (else BAD_REQUEST): engagementId — one call, meeting, or email thread in depth. Opaque string from Browse Engagements or this tool's citations; pass verbatim — some email IDs contain literal angle brackets, which are part of the ID; others are opaque tokens. zoominfoCompanyId — synthesize across an account's recent engagements. zoominfoContactId — synthesize across a contact's recent engagements. SAMPLING (IMPORTANT): Account and contact scopes quote from only the 5 most recent meetings and 5 most recent emails (meetings without stored transcripts are excluded). Account scope additionally draws on account-level intelligence synthesized across full history. For deeper coverage, drill into cited engagementIds. ENTRY POINT: Temporal or positional selector ("my most recent call") → enumerate with Browse Engagements (free, deterministic ordering), then pass the engagementId here. Thematic selector ("what concerns have they raised") → start account- or contact-scoped, then ask targeted follow-ups against a cited engagementId. Set includeContent: true (engagementId scope only, default false) only when verbatim text is required — it appends the raw transcript or email body, prefixed "Raw Engagement:"; long transcripts may be truncated; consumes significant context. IMPORTANT: Transcript/email body inclusion is controlled SOLELY by this boolean — never put transcript requests in the query field. If the user asks for a transcript, set this to true and keep the query focused on analytical intent (e.g. summary, key points). RETURNS: Synthesized answer with inline citations to source engagements, opening with a data-check line and ending with a Coverage section (window searched, N of M engagements reviewed, truncation notes) — read Coverage before treating an answer as exhaustive. DOES NOT HANDLE: Exhaustive topic search ("every call mentioning X") — sampling makes topic recall incomplete. Building a full engagement timeline or finding engagement IDs → Browse Engagements. Broad account orientation or company/market research → Account Research. Person background or profile research → Contact Research. Structured firmographic or demographic data for export → Enrich Companies / Enrich Contacts. QUALITY: Transcription, diarization, and inference are automated; attribution errors and omissions are possible — verify critical facts by calling this tool again with the cited engagementId and includeContent=true to retrieve the full transcript. CREDITS: Minimum 9 ZoomInfo AI credits per call, scaling with context retrieved and inference complexity.
conversation_intelligence
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({ "companies": [{ "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 🔁 **Chaining from a Search - "Get the full profile for a company I found":** For the query "Find fintech companies in NYC and give me the full profile on the top one", chain a search with an enrichment: 1. Call `search_companies` with your filters to find matching companies and their `companyId`s. 2. Call `enrich_company` with the `companyId` of the company you want full details on. **Example Workflow:** ``` 1. Call the industries lookup tool with fieldName: "industries" to find the exact id for the desired industry (e.g., "software.fintech"). 2. search_companies(industryList: ["<industry_id>"], metroRegion: "NY - New York City") → Returns companies with companyId: 55555, 66666, ... 3. enrich_company({ "companies": [{ "companyId": "55555" }], "requiredFields": ["revenue", "employeeCount", "companyFunding", "website"] }) → Returns full profile ``` 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_companies
Fetch multiple signal types for specific companies in a single request, enriching company details with Intent, News, and Scoops data. **Required:** Each request must include at least one zoominfoCompanyId and can enrich up to 10 companies at once. **Key Parameters:** • zoominfoCompanyIds - Array of ZoomInfo company IDs (1-10 company Ids per request) • signalTypes - Optional filter to specify which signals to return (INTENT, NEWS, SCOOP). If omitted, all three signal types are returned and sorted by latest date. **Example: "Get all signals for two companies":** ``` enrichCompanySignals(zoominfoCompanyIds: [123456789, 987654321]) ``` **Example: "Get only Intent and News signals for a company":** ``` enrichCompanySignals(zoominfoCompanyIds: [123456789], signalTypes: ["INTENT", "NEWS"]) ``` **Use Cases:** This unified endpoint gets the latest signals (intents, news, scoops) across multiple categories for a company in a single request, so it is ideal for getting a comprehensive picture of what's been happening with the company recently. Example questions: - What solutions is this account searching for right now? - What news has been published about them recently? - What are some recent big company changes? **Credit Usage:** This endpoint Charges data credits for any records returned for companies not currently under management. Companies enriched within the prior 12 months do not incur additional charges. It charges credits for each company enriched. Each signal (Intent, News, or Scoop) returned in the results counts as a Record, and a successful response counts as a Request credit.
enrich_company_signals
Enrich 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", "companyName": "Tech Corp"} ] }) ``` **SUPPORTED IDENTIFICATION METHODS:** For each contact, provide one of these valid combinations: • personId (most accurate) • email • phone • (firstName AND lastName AND companyName) • (fullName AND companyName) • (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_contacts
Use this tool to find similar companies for a given reference company. **Required:** Either zoominfoCompanyId or companyName must be provided. A request without one of these will fail with an error. The boolean filters alone are not sufficient to identify the reference company. Required: - Provide either zoominfoCompanyId (preferred) or companyName. At least one is mandatory. Optional: - sameRevenueRange, sameCountry, sameIndustry, sameEmployeeRange: boolean filters to narrow results to companies sharing that attribute with the reference company. - pageSize: number of results to return (default 25, max 100). 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 `zoominfoCompanyId` of the reference company, which identifies the company you want to use as the basis for finding similar companies. If `zoominfoCompanyId` 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. Optionally, you can narrow the results using the following boolean filters. When set to true, each filter restricts similar companies to those that share the corresponding attribute with the reference company: - `sameRevenueRange`: same revenue range - `sameCountry`: same country - `sameIndustry`: same industry - `sameEmployeeRange`: same employee count range 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`). To control the number of results returned, use the `pageSize` parameter with any integer value between 1 and 100 (default 25). 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_companies
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. 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
find_similar_contacts
Returns 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. Each entity includes a status field ('active' or 'archived') and an archivedAt timestamp when applicable, so archived configuration is always visible.
get_gtm_context
Fetch detailed data for a specific GTM Studio audience, including column definitions and an optional row preview. Use audience IDs from page context, browseAudiences, or upsertAudience; never generate or guess. For audienceAnalysis, use the returned audience ID as audienceId. 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_audience
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 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. Note that `reRankingScore` returns -1.0 when the ML reranking model did not run. **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. A `reRankingScore` of -1.0 indicates the reranking model did not run. - 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
get_recommended_contacts
Lookup 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 • **funding-round-types** - Funding round classifications (e.g., Seed, Series A) for recentFundingRoundTypes/allFundingRoundTypes filters • **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
lookup
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. Several parameters have `*List` variants (e.g. `companyTypeList`, `industryList`, `techAttributeTagIdList`) that accept typed arrays instead of comma-separated strings — prefer these for new integrations, passing each value as a separate list item. The original comma-separated/singular parameters remain supported for backward compatibility. 🔍 **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. search_companies(metroRegion: "CA - San Francisco", industryList: ["software"], employeeRangeMinimum: 100, employeeRangeMaximum: 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(techAttributeTagIdList: ["<product_id>"], 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 → techAttributeTagIdList (for technology stack searches) CREDITS: Free to use
search_companies
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. Several parameters have `*List` variants (e.g. `jobTitleList`, `managementLevelList`, `industryList`) that accept typed arrays instead of comma-separated strings — prefer these for new integrations, passing each value as a separate list item. The original comma-separated/singular parameters remain supported for backward compatibility. 🔍 **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(managementLevelList: ["VP Level Exec"], metroRegion: "CA - San Francisco", industryList: ["software"], employeeRangeMinimum: 100, employeeRangeMaximum: 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_contacts
Search 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_intent
Search 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.
search_scoops
Use 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. CREDITS: Free to use
submit_feedback
Update your organization's GTM configuration. Can synthesize unstructured content into structured records, or make direct changes to existing configuration. WHAT IT MANAGES: • Company Overview (name, elevator pitch, description, strategic priorities, custom fields) • Offerings / Products (value props, pain points, proof points, custom fields) • Buyer Personas (roles, objectives, pain points, priorities, custom fields) • Ideal Customer Profiles / ICPs (segments, firmographics, technographics, custom fields) • Competitors (products, strengths, weaknesses, win/loss reasons, custom fields) ENTITY LIFECYCLE - all entities (except Company Overview) support these state transitions: • active to archived: "archive this item" - soft-remove, hidden but recoverable • active to deleted: "delete this item" - permanent removal, irreversible • archived to active: "unarchive this item" - restore a previously archived item to active • archived to deleted: "delete this archived item" - permanently remove an archived item ARCHIVE vs DELETE (IMPORTANT): Archive is REVERSIBLE (the item can be restored later). Delete is PERMANENT and IRREVERSIBLE. When the user says "remove" or "get rid of" without explicitly saying "permanently delete" or "destroy", ALWAYS use archive, not delete. Only use delete when the user explicitly says "delete", "permanently delete", "permanently remove", or "destroy". UNARCHIVE / RESTORE: When the user says "unarchive", "restore", "reactivate", "bring back", or "make active again", use the unarchive action to move an archived entity back to active status. ACTIONS SUMMARY: The agent can Create, Read, Update, Archive, Unarchive, and Delete configuration (Offerings, Buyer Personas, Ideal Customer Profiles, Competitors). The Company Overview settings can only be created and updated (no archive or delete). Ask clarifying questions for destructive operations. If the user asks to delete many objects, make sure this is wanted and clarify requests to delete or archive objects. Ask clarifying questions for confusing requests. If the user creates when the object already exists, then ask for a confirmation to update the object. 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, unarchive, 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.
update_gtm_context
Recommended guideHow to get your ChatGPT Plugin suggestedFollow the practical steps agents use to find, shortlist, and choose your Plugin.Read guideZoomInfo ChatGPT Plugin FAQ
How the directory, categories and Discoverability Score work.
Read the methodologyHow do I improve ZoomInfo's ChatGPT Plugin discoverability?
The levers are the listing surface agents actually read: names, descriptions, keywords, tool metadata, and registry health. Which lever matters depends on where discovery breaks, which is what continuous measurement shows.
What are ZoomInfo alternatives on ChatGPT?
As of 2026-11-02, ZoomInfo competes with Add to CRM, AdvPulse, AgentData, AgentSource, AI Leads Scout, AI Vibe Prospecting, anVendor, Apollo.io and 71 more in ChatGPT B2B Prospecting & Contact Data, ranked by public Discoverability Score.
Where is this profile measured?
This profile uses the geography attached to the latest public registry snapshot: US. Locale tags are intentionally omitted.