Builds a searchable knowledge base from YouTube video transcripts with hybrid semantic and keyword search. Allows LLM assistants to search, organize, and retrieve timestamped information from videos you've watched.
Transforms YouTube into a queryable knowledge source with search, video details, transcript analysis, and AI-powered tools for summaries, learning paths, and knowledge graphs. Features quota-aware API access with caching and optional OpenAI/Anthropic integration for advanced content analysis.
Transforms YouTube videos into LLM-ready knowledge bases through transcription, semantic chunking, and vector embedding services. It provides 12 specialized MCP tools for video processing, semantic search, and SEO intelligence analysis.
Enables ingesting YouTube videos into a persistent wiki knowledge base by combining transcripts with scene-change frame analysis, then answering questions over the compiled pages via full-text search and agent reasoning. Serves raw candidate pages and a wiki table of contents to MCP clients so their own model can reason without requiring an API key.