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A JSON API for YouTube Transcript with MCP Support

Hacker News

A JSON API for YouTube Transcript with MCP Support

I built this because running yt-dlp in production (especially on serverless/Vercel) is a nightmare of IP blocks, cold starts, and binary dependencies. TranscriptAPI is a lightweight wrapper that handles the extraction, formatting, and proxy rotation. It prioritizes manual captions over auto-generated ones and returns clean JSON with timestamps, ready for RAG pipelines. The MCP (Model Context Protocol) Integration: I recently added native MCP support. If you use Claude Desktop or other MCP-compliant agents, you can add this API as a tool to 'watch' videos directly in your chat context without manually copying transcripts. Technical Stack: Backend: Python (FastAPI) on AWS Lambda (for burst scaling) Caching: Redis (to prevent hitting YouTube for the same video twice) Challenge: Handling 'drifting' timestamps in long livestreams where the auto-generated captions lose sync with the video frame. It has a free tier for hobbyists. I’m curious to hear how you’re handling the context-window limits when feeding full 3-hour transcripts to LLMs

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
26%26% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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