search_content

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Semantic search over content library using natural language queries and 768-D pgvector embeddings. WHEN TO USE: - Finding content by description or theme ("upbeat music videos", "cooking shows") - Discovering content similar to a concept or mood - Searching the content library without knowing exact titles or IDs - Content discovery for programmatic content scheduling RETURNS: - data: Array of matching content with similarity scores - videoId, title, contentCategory, description, durationSeconds - reviewStatus (approved/pending/rejected) - similarity (0-1, cosine similarity against query embedding) - meta: { count, query, limit, minSimilarity } EXAMPLE: User: "Find fitness and workout content" search_content({ query: "fitness workout exercise gym", limit: 10, min_similarity: 0.6 }) User: "Search for calming nature content suitable for medical offices" search_content({ query: "calming nature scenes peaceful landscapes meditation", min_similarity: 0.5 })