fetch
ChatGPTFetch one canonical Clozd knowledge record by ID after search returns it, such as deal:<uuid>, response:<uuid>, quote:<uuid>, theme:<uuid>, or competitor:<id>. Use when ChatGPT needs enough detail to cite, summarize, or continue analysis on a specific Clozd record. Returns a compact payload without raw transcripts, debug fields, emails, or internal implementation fields.
get_awe_deals
ChatGPTRetrieve deals flagged for action: At-Risk (likely to churn), Win Back (lost but recoverable), or Expansion (upsell opportunity). Use when the user asks "Which deals are at risk?" or "Show me win-back opportunities" or "How many expansion deals do we have?" Pass flag to filter to one type, or omit for all flagged deals. Returns deal list with flag type and optional counts per flag.
get_call_driver_categories
ChatGPTList call driver categories (high-level groupings, from Gong, Chorus, and/or SalesLoft) with aggregated sentiment distribution. Use when the user asks broad questions about call themes at the category level, e.g. "What are the main themes in our sales calls?" Returns: an array of categories, each with name, sentimentBreakdown, and totalMentions.
get_call_driver_category_counts
ChatGPTCount how many call driver mentions (Gong, Chorus, and/or SalesLoft) fall into each category. Use when the user wants a category-level frequency breakdown of call insights. Returns: an array of { categoryName, driverCount } sorted by driverCount descending.
get_call_driver_counts
ChatGPTRank call drivers (Gong, Chorus, and/or SalesLoft) by frequency of mention across filtered deals. Use when the user asks "What call drivers come up most often?" or "Which call themes are most common?" Returns an array of drivers with driverName and count, sorted by count descending. Pass limit to return only the top N drivers.
get_call_driver_overlap
ChatGPTCompare call drivers (Gong, Chorus, and/or SalesLoft) against Clozd interview-identified drivers to find agreement and gaps. Use when the user asks "How well do our call insights align with our interview data?" or "Where do call and interview data disagree?" Returns: { overlappingDealCount, nonOverlappingDealCount, totalDeals }.
get_call_drivers
ChatGPTList potential decision drivers automatically identified from call recordings (Gong, Chorus, and/or SalesLoft), with sentiment distribution. These are AI-detected signals from sales conversations, distinct from Clozd interview-based drivers. Use when the user asks about call insights, e.g. "What themes are coming up in our sales calls?" Returns: an array of drivers, each with name, sentimentBreakdown, and totalMentions.
get_competitor_sentiment_drivers
ChatGPTGet the decision drivers filtered by sentiment (positive or negative) in deals involving a specific competitor. Use sentiment "positive" when the user asks "Why do we beat Competitor X?" or "What are our strengths against Competitor X?" Use sentiment "negative" when the user asks "Why do we lose to Competitor X?" or "What are our weaknesses against Competitor X?" Returns: an array of { driverName, driverId, sentimentCount, totalMentions } for the specified competitor and sentiment.
get_competitors
ChatGPTList competitors encountered across deals in a program, along with encounter counts and win/loss counts. Use when the user asks which competitors show up most often, or how often the team wins or loses against specific competitors. Returns: an array of { competitorName, encounterCount, winCount, lossCount }.
get_deals
ChatGPTRetrieve a paginated list of deals. Each deal includes: name, outcome (win/loss), amount, business type (new/existing), business type detail, industry, closed date, published date, product, headcount, sales rep, region, and segment. Use when the user wants to browse, search, or export deal records. Example queries: "Show me all deals we lost last quarter" or "List deals over $100K in the healthcare industry."
get_decision_driver_categories
ChatGPTList decision driver categories (high-level groupings of drivers such as "Product", "Pricing", "Support") with aggregated sentiment distribution across a four-point scale (strong negative, negative, positive, strong positive). Use when the user asks broad thematic questions like "Which category of feedback is most negative?" Returns: an array of categories, each with name, sentimentBreakdown, and totalMentions.
get_decision_driver_category_counts
ChatGPTCount how many decision driver mentions fall into each category. Use when the user asks "How many driver mentions are in the Pricing category?" or wants a category-level frequency breakdown. Returns: an array of { categoryName, driverCount } sorted by driverCount descending.
get_decision_drivers
ChatGPTList individual decision drivers with aggregated sentiment distribution across a four-point scale (strong negative, negative, positive, strong positive). Use when the user asks which specific drivers are mentioned most often or most negatively, "What are the most common decision drivers?", "Which drivers come up most often?", or "What is the sentiment breakdown per driver?" Returns an array of drivers with driverName, totalMentions, and a sentimentBreakdown object containing strongNegative, negative, positive, and strongPositive counts. Sorted by totalMentions descending. Pass limit to return only the top N drivers. Supports filtering by outcome, competitor, date range, and other shared filters.
get_driver_quotes
ChatGPTRetrieve verbatim quotes from interview or survey responses linked to a specific decision driver or theme. Each quote includes the deal it came from plus respondent context such as title, channel, and publish date. Use when the user needs direct evidence, for example "Show me quotes about pricing" or "What did customers say about our support?" Provide either driverName for a case-insensitive text match or driverId for an exact UUID match.
get_gong_driver_categories
ChatGPTList Gong-identified driver categories (high-level groupings) with aggregated sentiment distribution. Use when the user asks broad questions about Gong call themes at the category level, e.g. "What are the main themes in our Gong calls?" Returns: an array of categories, each with name, sentimentBreakdown, and totalMentions.
get_gong_driver_category_counts
ChatGPTCount how many Gong-identified driver mentions fall into each category. Use when the user wants a category-level frequency breakdown of Gong call insights. Returns: an array of { categoryName, driverCount } sorted by driverCount descending.
get_gong_driver_counts
ChatGPTRank Gong-identified drivers by frequency of mention across filtered deals. Use when the user asks "What Gong drivers come up most often?" or "Which Gong themes are most common?" Returns an array of drivers with driverName and count, sorted by count descending. Pass limit to return only the top N Gong drivers.
get_gong_driver_overlap
ChatGPTCompare Gong-identified drivers against Clozd interview-identified drivers to find agreement and gaps. Use when the user asks "How well do Gong insights align with our interview data?" or "Where do Gong and Clozd disagree?" Returns: { overlappingDealCount, nonOverlappingDealCount, totalDeals }.
get_gong_drivers
ChatGPTList potential decision drivers automatically identified from Gong sales call recordings, with sentiment distribution. These are AI-detected signals from sales conversations, distinct from Clozd interview-based drivers. Use when the user asks about Gong call insights, e.g. "What themes are coming up in our sales calls?" Returns: an array of drivers, each with name, sentimentBreakdown, and totalMentions.
get_insight_gems
ChatGPTRetrieve Insight Gems — deals flagged with an actionable insight: At-Risk (likely to churn), Win-Back (lost but recoverable), Expansion (upsell opportunity), Feature Request (buyer asked for a specific product change), Sales Coaching (a coachable moment for the rep), or Case Study (a quotable customer success story). A program can also define its own custom gem types; they are returned alongside the built-ins and filtered the same way, by their custom_ key (e.g. custom_pricing_concern). Each gem carries gemKey (its type key) and gemName (its type's display name, which a program can change for a built-in type too; identify types by gemKey); the program's custom gem types also appear in counts by their custom_ key. list_filter_values with field gemTypes lists the program's valid types, and an unknown or unconfigured type returns an error listing them. Each gem has an open/resolved workflow status and by default only OPEN gems (still needing action) are returned — pass status to see resolved or all gems. Use when the user asks "Which deals are at risk?", "Show me win-back opportunities", or "What insight gems have we resolved?" Pass gemType to filter to one type (a built-in key or a custom gem's custom_ key), or omit for all types. Each gem includes its rationale (the reason it was flagged), who added it and how (addedByType: ai, clozd_consultant, or user), its owners (owner-field labels and literal emails) plus the resolved notification-recipient emails with notify/notificationDate, verbatim supporting quotes, any notes, and action taken / resolved-by details when resolved; counts per type are included by default. Data layer: ALL CRM DEALS — includes every CRM deal regardless of interview status. Counts are not comparable to PUBLISHED INTERVIEWS tools (e.g. get_deals, get_responses). Filtering by product or region is exact-match only. First call list_filters to see the filterable fields, then list_filter_values to enumerate a field's real values, and pass the exact values you want (arrays are supported). Do not guess names or use partial/wildcard strings. NOTE: PII redaction mode is enabled — personal names, emails, phone numbers, addresses, and company names in results are replaced with generic anonymized placeholders such as [Sales Rep], [Participant 1], [Company Name], and [Email Address].
get_programs
ChatGPTList the Clozd programs available to the authenticated user. Call this first to discover programId values for later program-scoped tools.
get_response_summaries
ChatGPTRetrieve AI-generated response summaries for published win-loss feedback. Pass responseId to fetch one response summary directly, or use shared deal and response filters to browse summaries in bulk with pagination.
get_responses
ChatGPTRetrieve a paginated list of win-loss responses (interviews and surveys). Each response includes the linked deal, outcome, respondent role, channel, and publish date so the LLM can find specific feedback submissions without loading full transcripts.
get_tags
ChatGPTList tags applied to deals with their usage counts. Tags are user-defined labels (e.g. "churn risk", "enterprise", "product gap") that categorize deals for filtering and analysis. Use when the user asks for a summary of tags, "What tags do we have?", "Which tags appear most often?", "Give me a breakdown of our tags", "Summarize deals by tag", or "How are deals tagged?" — even if they use words like "summary", "breakdown", or "overview". Do NOT use get_decision_drivers for tag-related questions. Set includeDeals=true to also return the deal IDs associated with each tag. Returns a paginated list of { tagName, count, dealIds? }.
get_transcripts
ChatGPTRetrieve full-text transcripts of win-loss interviews or surveys. Returns the complete conversation content. WARNING: Transcripts can be very long. Prefer get_response_summaries for quick insights or get_driver_quotes for targeted evidence. Use when the user explicitly requests raw transcripts, e.g. "Show me the full transcript for deal X." Pass responseId for a single transcript, or use filters with pagination for bulk retrieval.
get_win_rates
ChatGPTCalculate win/loss rates for deals in a program. Returns the percentage of deals won vs. lost, with optional breakdown by a single dimension (e.g. industry, product, sales rep). Use when the user asks questions like "What is our win rate?" or "Which industry has the highest win rate?" Returns: overall winRate, lossRate, totalDeals, and a breakdown array when groupBy is specified.
get_workflow_deal_participants
ChatGPTRetrieve participants for a specific deal in a workflow, including each participant's approval status (pending/approved/rejected), outreach status, and who approved or rejected them. Returns the deal's current workflow stage and a list of participants with their individual status details. Use after get_workflow_deals to drill into participant-level detail for a specific deal. Example queries: "Who are the participants for this deal?" or "Has anyone approved this deal yet?"
get_workflow_deals
ChatGPTRetrieve a paginated list of deals in a workflow with their stage, status, and participant count. Each deal includes: name, outcome, amount, sales rep, stage (needs_attention, needs_approval, ready_for_distribution, in_progress, completed), and a participant_count. To get full participant details (approval status, who approved/rejected), use the get_workflow_deal_participants tool with a specific dealId. Example queries: "Show me deals needing approval in this workflow" or "Which deals are ready for distribution?"
get_workflows
ChatGPTRetrieve all workflows in a program with their configuration, filter rules, settings, and deal/participant counts. Each workflow includes: name, status, feedback channel, interview type, deal rules, participant rules, outreach settings, contributor settings, and counts by stage (needs_attention, needs_approval, ready_for_distribution, in_progress, completed). Use when the user wants to understand the workflow pipeline for a program. Example queries: "Show me all active workflows" or "What are the outreach settings for our buyer workflows?"
list_filter_values
ChatGPTList the valid or observed values for a single deal filter field in a program. Pass a field returned by list_filters. The response has: valueSource (enum | reference | crm_observed | metadata_picklist | metadata_observed), exhaustive (true when the values are the complete valid set, false when they are only values seen in the data), truncated (observed lists are capped at 100 by deal count), and values (each an optional dealCount; a null value is the blank/unspecified bucket). Call this to get exact filter values before filtering get_deals, so you pass real values instead of guessing strings.
list_filters
ChatGPTList the filterable fields for a program's deals — the standard Clozd fields (product, region, industry, segment, outcome, businessType, decision, gemTypes, leadSource). Each entry has: field (the key to pass to list_filter_values and to get_deals), displayName, valueType, and exhaustive (whether the field has a complete known value set). Call this first to discover what you can filter by, then call list_filter_values for a field's values before filtering get_deals. The catalog also includes the program's custom deal-metadata fields (valueType metadata_picklist or metadata_observed) — filter get_deals by these via its customFilters parameter.
search
ChatGPTSearch Clozd win-loss knowledge for relevant deals, feedback summaries, themes, competitors, and quotes using a natural-language query plus optional filters. Use this first when the user asks to find, explore, cite, compare, or summarize Clozd feedback but does not already have a specific record ID. Returns compact canonical record IDs that can be passed to fetch for more detail.
fetch
Claudeget_call_driver_categories
Claudeget_call_driver_category_counts
Claudeget_call_driver_counts
Claudeget_call_driver_overlap
Claudeget_call_drivers
Claudeget_competitor_sentiment_drivers
Claudeget_competitors
Claudeget_deals
Claudeget_decision_driver_categories
Claudeget_decision_driver_category_counts
Claudeget_decision_drivers
Claudeget_driver_quotes
Claudeget_insight_gems
Claudeget_organizations
Claudeget_programs
Claudeget_response_summaries
Claudeget_responses
Claudeget_tags
Claudeget_transcripts
Claudeget_win_rates
Claudeget_workflow_deal_participants
Claudeget_workflow_deals
Claudeget_workflows
Claudelist_filter_values
Claudelist_filters
Claudesearch
Claude
