ClipSense MCP Server

# DevHunt Launch Post ## Product Name ClipSense MCP ## Tagline AI debugging for mobile apps through video analysis ## Alternative Taglines 1. Debug mobile apps with AI-powered video analysis 2. Turn crash videos into actionable code fixes with AI 3. Frame-by-frame AI analysis for mobile debugging 4. Analyze 500MB crash videos with AI in your IDE ## Short Description (280 characters max) AI-powered debugging for React Native, iOS, and Android apps. Record your crash → paste video path → get root cause + code fixes in 2 minutes. Works directly in VS Code, Cursor, Windsurf via Model Context Protocol. 500MB videos supported, 9 formats. ## Full Description ### The Problem Mobile developers waste hours debugging from screen recordings. Users report bugs with 50-500MB videos, but existing tools can't handle them: - Claude Desktop: 31MB file limit (99% of videos exceed this) - Loom comments: Lack technical depth - Manual review: Takes hours per video - Bug reproduction: Inconsistent and time-consuming ### The Solution ClipSense MCP brings AI-powered video analysis directly into your coding workflow using Model Context Protocol. **How it works:** 1. Record your app crash with QuickTime, iOS, or Android screen recording 2. In your IDE: "Analyze Desktop/crash.mp4" 3. Get comprehensive analysis in ~2 minutes: - Root cause identification (e.g., "NullPointerException at ProfileScreen.tsx:142") - Timeline breakdown (frame-by-frame key moments) - Visual evidence (frames where bug manifests) - Code fix suggestions (copy-paste ready) **Powered by Claude Sonnet 4.5** for frame-by-frame vision analysis. ### Key Features - **500MB video support** (vs Claude Desktop's 31MB limit) - **9 video formats**: MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV - **Multi-platform**: React Native, iOS (Swift/Objective-C), Android (Kotlin/Java) - **IDE integration**: Works in VS Code (Claude Code), Cursor, Windsurf, Continue.dev, Cline - **Fast processing**: 2-3 minutes average - **Secure**: Videos encrypted, auto-deleted after 24 hours ### Tech Stack - TypeScript - Model Context Protocol SDK - Node.js 18+ - Cloudflare R2 (encrypted video storage) - Claude Sonnet 4.5 (AI vision) - PostgreSQL (job queue) ### Use Cases 1. **React Native crash debugging**: Identify null pointer exceptions from user-reported crashes 2. **UI/UX bug analysis**: Detect layout issues, visual glitches, animation problems 3. **Performance debugging**: Analyze slow renders, janky scrolling, memory issues 4. **Integration debugging**: Track down API errors, network failures 5. **QA workflow**: Automate video bug report analysis ### Why Developers Love It - No context switching (works directly in IDE) - No manual video scrubbing - Actionable code fixes (not just descriptions) - Works with existing screen recording tools - Free tier available (3 analyses/month) ### Example Output ```markdown ## Root Cause Null pointer exception when accessing user.profile.avatar at ProfileScreen.tsx:142 ## Timeline - 0:00-0:15 - User navigates to profile screen - 0:15-0:18 - App attempts to load avatar image - 0:18 - Crash occurs (NullPointerException) ## Visual Evidence 127 frames analyzed Key moments: - 0:15 (Frame 23): Profile screen rendered, avatar placeholder visible - 0:18 (Frame 24): White screen (crash) ## Recommended Fix Add null check before accessing avatar: javascript const avatarUrl = user?.profile?.avatar ?? DEFAULT_AVATAR; ## Next Steps 1. Add null safety checks in ProfileScreen.tsx:142 2. Implement error boundary for profile component 3. Add fallback UI for missing user data ``` ### Pricing - **FREE**: 3 analyses per month, no credit card required - **PRO ($29/mo)**: 50 analyses per month - **TEAM ($99/mo)**: 300 analyses per month, team collaboration - **ENTERPRISE**: Custom pricing for large teams ### Getting Started 1. Get free API key: ```bash curl -X POST "https://api.clipsense.app/api/v1/keys/request" \ -H "Content-Type: application/json" \ -d '{"email":"your-email@example.com"}' ``` 2. Install MCP server: ```bash npm install -g @gburanda/clipsense-mcp-server ``` 3. Configure your IDE: Add to your MCP settings file: ```json { "mcpServers": { "clipsense": { "command": "npx", "args": ["-y", "@gburanda/clipsense-mcp-server"], "env": { "CLIPSENSE_API_KEY": "cs_sk_YOUR_API_KEY_HERE" } } } } ``` 4. Start debugging: ``` Analyze Desktop/app-crash.mp4 ``` ### Links - GitHub: https://github.com/clipsense/-mcp-server - Website: https://clipsense.app - Documentation: https://github.com/clipsense/-mcp-server#readme - npm: https://www.npmjs.com/package/@gburanda/clipsense-mcp-server ### Target Audience - React Native developers - iOS developers (Swift/Objective-C) - Android developers (Kotlin/Java) - Mobile QA engineers - DevOps teams managing mobile CI/CD - Technical support teams analyzing bug reports ### Keywords mobile debugging, AI debugging, video analysis, React Native, iOS development, Android development, crash analysis, bug detection, Model Context Protocol, MCP, Claude AI, developer tools, debugging tools, screen recording analysis --- ## DevHunt Launch Strategy ### Pre-Launch (1 week before) - [ ] Create product listing on DevHunt - [ ] Upload demo GIF/video - [ ] Upload product screenshots (5-7 images) - [ ] Upload logo - [ ] Schedule launch for Tuesday-Thursday (best engagement days) - [ ] Prepare social media posts - [ ] Email existing users (if any) to ask for upvotes ### Launch Day Checklist - [ ] Post product at 12:01 AM PT (beginning of launch day) - [ ] Share on Twitter with #DevHunt hashtag - [ ] Share in relevant Reddit communities - [ ] Post in Discord/Slack communities (React Native, iOS dev, Android dev) - [ ] Reply to ALL comments within first 2 hours - [ ] Monitor engagement throughout the day - [ ] Thank supporters publicly ### Post-Launch (first week) - [ ] Follow up with commenters - [ ] Share user testimonials - [ ] Post "We launched on DevHunt" recap on Twitter/LinkedIn - [ ] Analyze metrics (upvotes, clicks, conversions) - [ ] Implement feedback from DevHunt community ### Success Metrics - **Good launch**: 50+ upvotes, 10+ comments - **Great launch**: 100+ upvotes, 25+ comments, top 5 product of the day - **Exceptional launch**: 200+ upvotes, 50+ comments, #1 product of the day - **Conversions**: 100+ API key requests from DevHunt traffic (track with utm_source=devhunt) ### Best Launch Date/Time - **Day**: Tuesday, Wednesday, or Thursday (avoid Monday and Friday) - **Week**: January 7-9, 2026 (avoid holiday weeks) - **Time**: Launch at 12:01 AM PT (beginning of day) ### Community Engagement Tips 1. Reply to every comment within 1 hour 2. Be humble about limitations 3. Offer free trials to commenters who ask questions 4. Ask for specific feedback ("What other features would help?") 5. Thank people for trying it 6. Don't be defensive about criticism 7. Share technical details when asked ### Promotion Strategy **Twitter:** - Post launch announcement with demo GIF - Tag @devhuntapp - Use hashtags: #DevHunt #MCP #AI #ReactNative #iOSDev **Reddit:** - r/reactnative (allow self-promotion on weekends) - r/iOSProgramming (check self-promotion rules) - r/androiddev (check self-promotion rules) - r/webdev (broader audience) - r/SideProject (show HN style post) **Discord/Slack:** - React Native Discord - Expo Discord - iOS Developers Slack - Android Developers Slack **LinkedIn:** - Share as personal update (not company page) - Tag relevant people in mobile dev community - Ask connections to engage --- ## Assets Needed for Launch ### Required - [x] Product description (complete) - [ ] Demo GIF (30 seconds, <10MB) - [ ] Product screenshots (5-7 images showing): 1. Installation/setup 2. Example usage in IDE 3. Analysis results 4. Comparison table 5. Pricing page 6. Documentation page 7. Error handling - [ ] Logo (PNG, 512x512) - [ ] Website link (https://clipsense.app) - [ ] GitHub link (https://github.com/clipsense/-mcp-server) ### Optional (but recommended) - [ ] Product demo video (1-2 minutes on YouTube) - [ ] Testimonials from early users - [ ] Twitter/LinkedIn profile links - [ ] Maker profile (your background, why you built this) --- ## Sample Social Media Posts for Launch Day ### Twitter 🚀 Just launched ClipSense MCP on @devhuntapp! Debug mobile apps with AI-powered video analysis. Record crash → paste path → get root cause + fixes in 2 min. ✅ 500MB videos (vs Claude Desktop's 31MB) ✅ Works in VS Code/Cursor/Windsurf ✅ React Native, iOS, Android Try it: [DevHunt link] ### LinkedIn I'm excited to share ClipSense MCP, which we just launched on DevHunt! As mobile developers, we waste hours manually scrubbing through crash videos. ClipSense uses AI to analyze 500MB screen recordings and provide: • Root cause identification • Frame-by-frame timeline • Code fix suggestions Built with Model Context Protocol, it works directly in VS Code, Cursor, and Windsurf. Free tier: 3 analyses/month. No credit card needed. Check it out: [DevHunt link] ### Reddit (r/reactnative) [Title] I built an AI debugging tool for React Native crash videos Hey everyone! I built ClipSense MCP to help debug mobile apps from screen recordings. **The problem:** Users send 50-500MB crash videos. Claude Desktop has a 31MB file limit. Manual review takes hours. **The solution:** ClipSense uses Model Context Protocol to analyze videos with AI (Claude Sonnet 4.5) and return root cause + code fixes. **How it works:** 1. Record crash with QuickTime/iOS/Android 2. In VS Code: "Analyze Desktop/crash.mp4" 3. Get analysis in ~2 minutes **Tech:** TypeScript, MCP SDK, Cloudflare R2, Claude Sonnet 4.5 **Free tier:** 3 analyses/month We just launched on DevHunt: [link] GitHub: https://github.com/clipsense/-mcp-server Would love your feedback!