ZoomInfo

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