framework_to_insight
ChatGPTConvert structured analytical frameworks into insights. This tool takes a pre-defined analytical framework (structured query parameters) and executes it to generate insights. Useful when you have specific query parameters rather than natural language questions. This provides more control than natural language queries and is ideal for programmatic access or when you know exactly what data structure you need.
get_entities_from_insight
ChatGPTGet entities related to a specific insight. Retrieves entities associated with a particular insight. Useful for exploring what entities are involved in a specific insight or understanding insight-entity relationships. Valid entity_representation values: abundle, actor, aingredient, app, apublisher, artist, category, cbsa, city, clfiling, clopinion, company, country, dccategory, ddcategory, director, distributor, dma, dtcategory, educationlevel, entertainer, ethnicity, finished, franchise, gender, generation, genre, judge, label, lawyer, league, legalmatter, maritalstatus, medicalsegment, mine, occupation, pcodecategory, plabel, platform, port, product, raw, region, retailer, service, socialhandle, song, sport, state, team, ticker, title, transformed, us, vehiclebody, vehiclemake, vehiclemodel, venue, website, ww, yearlyincome, zip
get_insights_from_entity
ChatGPTGet insights related to a specific entity. Retrieves insights associated with a particular entity (e.g., company, state, retailer). Useful for exploring what data is available for a specific entity or understanding entity-insight relationships. Valid entity_representation values: abundle, actor, aingredient, app, apublisher, artist, category, cbsa, city, clfiling, clopinion, company, country, dccategory, ddcategory, director, distributor, dma, dtcategory, educationlevel, entertainer, ethnicity, finished, franchise, gender, generation, genre, judge, label, lawyer, league, legalmatter, maritalstatus, medicalsegment, mine, occupation, pcodecategory, plabel, platform, port, product, raw, region, retailer, service, socialhandle, song, sport, state, team, ticker, title, transformed, us, vehiclebody, vehiclemake, vehiclemodel, venue, website, ww, yearlyincome, zip
search_entities
ChatGPTSearch for entities by name or description. Optionally filter by entity_representation to narrow results to a specific entity type. See the entity_representation parameter for valid values. Valid entity_representation values: abundle, actor, aingredient, app, apublisher, artist, category, cbsa, city, clfiling, clopinion, company, country, dccategory, ddcategory, director, distributor, dma, dtcategory, educationlevel, entertainer, ethnicity, finished, franchise, gender, generation, genre, judge, label, lawyer, league, legalmatter, maritalstatus, medicalsegment, mine, occupation, pcodecategory, plabel, platform, port, product, raw, region, retailer, service, socialhandle, song, sport, state, team, ticker, title, transformed, us, vehiclebody, vehiclemake, vehiclemodel, venue, website, ww, yearlyincome, zip
search_insights
ChatGPTSearch for insights by name or description.
text_to_insight
ChatGPTConvert natural language questions into structured analytical frameworks and insights. This tool transforms business questions into data queries and returns results in markdown table format. It automatically identifies relevant entities, metrics, and filters based on your question. Common Use Cases: - Revenue and spend analysis by company, region, or time period - Growth metrics and trend analysis - Comparative analysis across entities and insights - Performance benchmarking and ranking across entities and insights Not Suitable For: - Data discovery questions: "What data is available for Walmart?" - Data discovery questions: "What fields can I filter on?"
