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108,874 tools. Last updated 2026-04-17 00:21
  • Search observations by semantic similarity. Find moments that match a description like "lunch rush at fast casual restaurants" using vector embeddings. Uses 768-dimensional Gemini embeddings on observation payloads to find promoted observations matching a natural language query via pgvector cosine similarity search. WHEN TO USE: - Finding observations that match a conceptual description - Discovering contextual moments across the screen network - Searching for audience situations ("families waiting in line", "professionals on coffee break") - Finding commerce patterns ("high purchase intent near checkout") RETURNS: - data: Array of matching observations ranked by semantic similarity, each with: - observation_id, device_id, venue_type, observation_family - observed_at, payload, confidence, evidence_grade - similarity: Cosine similarity score (0-1, higher = more relevant) - metadata: { result_count, query_embedding_model, search_scope } - suggested_next_queries: Related semantic queries to explore EXAMPLE: User: "Find lunch rush moments at fast casual restaurants" semantic_search_observations({ query: "lunch rush at fast casual restaurants with high foot traffic", filters: { venue_type: ["restaurant_qsr"] }, limit: 20 }) User: "Find moments with high emotional engagement" semantic_search_observations({ query: "audience showing strong positive emotional reactions", filters: { observation_family: ["audience"] }, limit: 10 })
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  • Explain a single finding in natural language. Requires the finding as a JSON dict and the file_path to load a profile for context.
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  • List entities of a specific type/category in a location. Great for questions like 'What restaurants are in Nashville?' or 'Find dentists in Austin, TX'. Results are ranked by verification tier and AI visibility score.
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