ClipSense MCP Server
The ClipSense MCP Server enables AI coding assistants to analyze local mobile app bug videos and suggest code fixes directly within your development workflow.
Analyze video files showing mobile app bugs, crashes, UI issues, and unexpected behavior
Support multiple platforms: React Native, iOS (Swift/Objective-C), and Android (Kotlin/Java)
Process various video formats: MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV (up to 500MB, 10 minutes)
AI-powered debugging using Claude Sonnet 4.5 for frame-by-frame analysis
Generate comprehensive analysis including root cause identification, timeline breakdown, visual evidence, and actionable code fix suggestions
Handle custom analysis questions to focus on specific aspects like network requests or UI rendering
Integrate with MCP-compatible AI coding assistants (Claude Code, Cursor, Windsurf, Continue.dev, Cline, etc.)
Process videos in 2-3 minutes for most cases
Maintain data security with encrypted uploads and automatic 24-hour deletion
Analyzes screen recording videos of Android mobile apps to identify bugs, crashes, UI issues, and provides code fix suggestions for Kotlin and Java applications.
Automatically converts various video formats (MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV) to MP4 for processing and video compression.
Analyzes screen recording videos of iOS mobile apps to identify bugs, crashes, UI issues, and provides code fix suggestions for Swift and Objective-C applications.
Analyzes screen recording videos of React Native mobile apps to identify bugs, crashes, UI issues, and provides AI-powered debugging recommendations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ClipSense MCP Serveranalyze this crash video from my React Native app"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ClipSense MCP Server
Model Context Protocol (MCP) server for ClipSense - AI-powered mobile debugging through video analysis.
Analyze screen recordings of mobile app bugs with AI coding assistants. Get instant insights into crashes, UI issues, and unexpected behavior in React Native, iOS, and Android apps.
Compatibility
Works with multiple AI coding assistants that support MCP (Model Context Protocol):
Claude Code (VS Code extension) - Full filesystem access, handles videos up to 500MB
Cursor - Full MCP support via
.cursor/mcp.jsonWindsurf - MCP support via
.windsurf/mcp.jsonCline - Full MCP support with integrated marketplace
Roo-Cline/Roo Code - MCP support with manual configuration
Continue.dev - MCP support for VS Code and JetBrains IDEs
OpenAI Codex - Comprehensive MCP support
Important Limitations:
Claude Desktop: Does NOT work due to 31MB file upload limit (99.99% of videos exceed this)
One-time analysis: Each analysis is independent; follow-up questions require re-analyzing the video
Related MCP server: MCP Performance Analyzer
Features
Video Bug Analysis: Analyze local screen recordings showing mobile app issues
AI-Powered Debugging: Claude Sonnet 4.5 analyzes videos frame-by-frame to identify problems
Multi-Platform Support: Works with React Native, iOS (Swift/Objective-C), Android (Kotlin/Java)
Code Fix Suggestions: Get actionable recommendations to fix bugs
Fast Processing: Results in ~2 minutes for most videos
Why ClipSense?
Feature | ClipSense MCP | Claude Desktop | Manual Debugging | Screen Recording Tools |
Video file size limit | 500MB | 31MB | N/A | Varies |
AI analysis | ✅ Claude Sonnet 4.5 | ✅ (limited by file size) | ❌ Manual review | ❌ No analysis |
Works in AI coding assistants | ✅ All MCP-compatible | ❌ Desktop only | ❌ | ❌ |
Code fix suggestions | ✅ Contextual | ✅ (limited) | ❌ | ❌ |
Timeline breakdown | ✅ Automatic | ❌ | ❌ Manual | ❌ |
Root cause analysis | ✅ Automatic | ✅ (limited) | ❌ Manual | ❌ |
Multi-format support | ✅ 9 formats | ✅ (limited) | ✅ | ✅ |
Integration with workflow | ✅ Native in IDE | ❌ Separate app | ❌ | ❌ |
Processing time | 2-3 minutes | Instant (upload limited) | Hours | N/A |
Bottom line: ClipSense is the only tool that brings AI-powered video analysis directly into your development workflow, with support for realistic file sizes and multiple video formats.
Demo

From crash video to root cause in under 3 minutes
Installation
1. Get Your API Key
Request a free API key:
curl -X POST "https://api.clipsense.app/api/v1/keys/request" \
-H "Content-Type: application/json" \
-d '{"email":"your-email@example.com"}'Check your email for the API key (starts with cs_sk_).
2. Install via npm
npm install -g @gburanda/clipsense-mcp-server3. Configure Your AI Coding Assistant
Claude Code (VS Code Extension)
Add to your MCP settings file:
macOS/Linux: ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json
Windows: %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json
{
"mcpServers": {
"clipsense": {
"command": "npx",
"args": ["-y", "@gburanda/clipsense-mcp-server"],
"env": {
"CLIPSENSE_API_KEY": "cs_sk_YOUR_API_KEY_HERE"
}
}
}
}Cursor
Create or edit .cursor/mcp.json in your project root:
{
"mcpServers": {
"clipsense": {
"command": "npx",
"args": ["-y", "@gburanda/clipsense-mcp-server"],
"env": {
"CLIPSENSE_API_KEY": "cs_sk_YOUR_API_KEY_HERE"
}
}
}
}Windsurf
Create or edit .windsurf/mcp.json in your project root:
{
"mcpServers": {
"clipsense": {
"command": "npx",
"args": ["-y", "@gburanda/clipsense-mcp-server"],
"env": {
"CLIPSENSE_API_KEY": "cs_sk_YOUR_API_KEY_HERE"
}
}
}
}Continue.dev
Add to your Continue configuration (VS Code or JetBrains):
{
"mcpServers": {
"clipsense": {
"command": "npx",
"args": ["-y", "@gburanda/clipsense-mcp-server"],
"env": {
"CLIPSENSE_API_KEY": "cs_sk_YOUR_API_KEY_HERE"
}
}
}
}Cline, Roo-Cline, OpenAI Codex
Refer to your IDE's MCP configuration documentation. The server configuration follows the same pattern shown above.
4. Restart Your IDE
The ClipSense MCP tool will now be available in your AI coding assistant.
Usage
In your AI coding assistant, simply ask to analyze a bug video:
Analyze this bug video: /Users/me/Desktop/app-crash.mp4Your AI assistant will use the ClipSense MCP server to:
Upload your video
Process it with AI vision (Claude Sonnet 4.5)
Identify the bug and suggest fixes
Follow-up Questions
Each analysis is independent. To ask follow-up questions about the same video:
Analyze /Users/me/Desktop/app-crash.mp4 and focus on the network request flowThe video will be re-analyzed with your new question. Follow-up questions without re-specifying the video path will not have access to the previous analysis context.
Supported Video Formats
All common formats supported:
MP4, MOV, WebM, AVI, MKV, FLV, MPEG/MPG, 3GP, WMV
Max file size: 500MB
Max duration: 10 minutes
All formats automatically converted to MP4 for processing
Example Questions
"Why does my app crash when I tap the login button?"
"What's causing this UI glitch on the profile screen?"
"Analyze this video and explain what's happening"
"What could be causing this infinite scroll issue?"
Example Analysis Output
When you analyze a video, ClipSense provides:
# ClipSense Analysis Complete 🔍
## 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
Add null safety checks in ProfileScreen.tsx
Implement error boundary for profile component
Add fallback UI for missing user data
💬 Have follow-up questions? Continue this conversation with your AI assistant 📊 View full details: https://clipsense.app/results/job_abc123 📝 Analysis ID: job_abc123
## 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)**: Contact sales for custom pricing
> **Note:** Pricing reflects current backend implementation. Contact support@clipsense.app for custom plans.
## FAQ
### What video formats are supported?
All common video formats: MP4, MOV, WebM, AVI, MKV, FLV, MPEG/MPG, 3GP, and WMV files up to 500MB and 10 minutes duration. All formats are automatically converted to MP4 for processing.
### How long does analysis take?
Most videos are analyzed in 2-3 minutes. Longer videos (5+ minutes) may take up to 5 minutes.
### Is my video data secure?
Yes. Videos are encrypted during upload and storage. All videos are automatically deleted after 24 hours. We never train AI models on your data.
### Can I ask follow-up questions about an analysis?
Each MCP analysis is independent. To explore different aspects of the same bug, re-analyze the video with a more specific question (e.g., "focus on the network request flow").
### What if I run out of free analyses?
You can upgrade to PREMIUM ($19/mo) for unlimited analyses, or wait until next month when your free tier resets.
## Troubleshooting
### Error: "CLIPSENSE_API_KEY not found"
**Solution:** Ensure your MCP settings file has the correct `env` section with your API key:
```json
"env": {
"CLIPSENSE_API_KEY": "cs_sk_YOUR_KEY_HERE"
}Restart your IDE after updating.
Error: "Command not found: npx"
Solution: Install Node.js (v18+) from nodejs.org. Then restart your terminal and IDE.
Error: "Upload failed" or "File too large"
Solution:
Ensure video is under 500MB
Trim video to show only the relevant bug (crash moment + 10 seconds before)
Compress with:
ffmpeg -i input.mp4 -vcodec h264 -acodec aac output.mp4
Analysis stuck at "processing"
Solution:
Check status at https://clipsense.app/results/[job_id]
If stuck for 10+ minutes, contact support@clipsense.app with the job ID
Claude Code doesn't see the ClipSense tool
Solution:
Verify MCP settings file location matches your IDE
Check for JSON syntax errors in your MCP config
Restart VS Code completely (Cmd+Q, not just window close)
Try
npx @gburanda/clipsense-mcp-servermanually to test installation
Support
Documentation: https://clipsense.app/docs
Email: support@clipsense.app
License
MIT
Available Tools
1 toolanalyze-videoA
Use this tool to analyze video files on the user's computer that show mobile app bugs. Reads local video files (MP4, MOV, WebM, AVI, MKV, FLV, MPEG, 3GP, WMV) and provides AI-powered analysis to identify errors, crashes, UI issues, and suggests code fixes. Works with React Native, iOS (Swift/Objective-C), and Android (Kotlin/Java) apps. Use this when the user asks you to analyze, examine, or debug a video file showing app behavior.
| Name | Required | Description | Default |
|---|---|---|---|
| videoPath | Yes | Absolute path to the video file on the user's computer (e.g., /Users/username/Desktop/bug.mp4). Max 500MB, max 10 minutes. | |
| question | No | Specific question about the bug (optional). Example: 'Why does the button not respond when tapped?' If not provided, a general analysis will be performed. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it reads local files (implies no network calls), lists supported video formats, mentions AI-powered analysis, and specifies the types of issues identified. However, it doesn't cover important aspects like performance characteristics (processing time), error handling (what happens with invalid files), or output format details. The description adds value but leaves gaps in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds value: supported formats, analysis capabilities, supported platforms, and usage guidance. There's minimal redundancy, though the final sentence could be slightly more concise. Overall, it's well-structured with information density.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (video analysis with AI) and lack of both annotations and output schema, the description does an adequate but incomplete job. It covers the what and when reasonably well but lacks details about the analysis output format, limitations (beyond file size/duration in schema), error conditions, and processing behavior. For a tool with no structured output documentation, more completeness would be beneficial.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing good documentation for both parameters. The description doesn't add significant meaning beyond the schema - it mentions video files and analysis questions generally but doesn't elaborate on parameter usage, constraints, or examples beyond what's in the schema descriptions. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with additional parameter insights.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('analyze video files', 'reads local video files', 'provides AI-powered analysis') and resources ('video files on the user's computer', 'mobile app bugs'). It distinguishes what it analyzes (video files showing app bugs) and what it provides (error identification, crash detection, UI issue spotting, code fix suggestions). No siblings exist, but the description is sufficiently specific.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool ('when the user asks you to analyze, examine, or debug a video file showing app behavior') and specifies supported platforms (React Native, iOS, Android). However, it doesn't mention when NOT to use it or alternatives (though no siblings exist, so this is less critical). The guidance is explicit but lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
- First observed
analyze-video
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'analyze-video' has a singular, well-defined purpose that cannot be confused with any other tool in the set.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'analyze-video' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.
A single tool is too few for a server named 'ClipSense MCP Server' that implies a broader video analysis or debugging domain. While the tool is well-described, the server lacks additional tools for related operations like listing videos, managing analyses, or handling different file types beyond analysis, making it feel thin and incomplete for its apparent scope.
The server is severely incomplete for video analysis and debugging. It only provides analysis, with no tools for uploading, listing, deleting, or managing video files, nor for handling analysis results or integrating with development workflows. This creates significant gaps that will likely cause agent failures in real-world use.
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