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I built an MCP to help build Cursor for Video Editing

Hacker News

I built an MCP to help build Cursor for Video Editing

Hi HN! I spent the last weekend exploring Anthropic’s Model Context Protocol (MCP) and wanted to share a starter kit for anyone who wants to build a cursor for video/audio in minutes. Backstory I realized it’d be neat to have a “batteries-included” MCP server that could handle common video and audio tasks. So I used Claude 3.7 and Gemini 2.5 Pro, vibe coded to scaffold the repo, bake in automated tests, and pushed to Smithery. This Python-based MCP server exposes tools for: Core Video Operations extract_audio_from_video – Extract audio tracks from video files trim_video – Cut video segments with precise timing convert_video_format – Convert between video formats (MP4, MOV, AVI, etc.) convert_video_properties – Comprehensive video property conversions change_aspect_ratio – Adjust aspect ratios with padding or cropping set_video_resolution – Change resolution while preserving quality set_video_codec – Switch codecs (H.264, H.265, VP9, etc.) set_video_bitrate – Tweak bitrate for quality vs. file size set_video_frame_rate – Modify playback FPS Audio Processing convert_audio_format – Convert between audio formats (MP3, WAV, AAC, etc.) convert_audio_properties – Full audio property conversions set_audio_bitrate – Adjust audio quality/compression set_audio_sample_rate – Change sample rates set_audio_channels – Convert mono ↔ stereo set_video_audio_track_codec – Change a video’s embedded audio codec set_video_audio_track_bitrate – Adjust embedded audio bitrate set_video_audio_track_sample_rate – Change embedded audio sample rate set_video_audio_track_channels – Adjust embedded audio channels Creative Tools add_subtitles – Burn styled subtitles into video add_text_overlay – Overlay dynamic text with timing cues add_image_overlay – Insert watermarks or logos add_b_roll – Splice in B-roll footage with transitions add_basic_transitions – Apply fade-in/out effects Advanced Editing concatenate_videos – Join multiple clips (optional transitions) change_video_speed – Create slow-motion or time-lapse effects remove_silence – Auto-trim silent segments Diagnostics health_check – Verify server is up and all tools respond All of these live in a single server.py, backed by a full pytest suite in tests/. Try it Out! Github: https://github.com/misbahsy/video-audio-mcp Smithery: https://smithery.ai/server/@misbahsy/video-audio-mcp

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios, gemini · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, claude, model · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, video · Missing: mobile apps, personal, entrepreneurs
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
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
22%22% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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