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mcp

YouTube transcript search for your AI. Find who said what with timestamps, mentions, sponsors, and recommendations. Remote MCP and agent skills.

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GITHUB · launched Oct 12, 2026 · 33 upvotes (top 39%)

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Could you build this?

Partial While the MCP interface wrapper is trivial to build, indexing, transcribing, and running entity/sponsor extraction across massive YouTube video archives requires significant crawling and data pipeline infrastructure.

What it would actually take: A production version requires a distributed ingestion pipeline using yt-dlp and Whisper/deepgram for transcription, combined with LLM-based entity and sponsorship tagging pipelines. Transcripts and vectors must be stored in an index like Elasticsearch or Qdrant and served via an MCP server written in Node/TypeScript or Python. Specialized knowledge in data scraping resilience, speech-to-text cost optimization, and vector search at scale is required.

Competitors

Other products that read as similar to this one : 211 launches clear the similarity bar, closest 8 shown.

Attention rank: #107 of 212 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 346 days after the earliest competitor.

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