nihitgupta2/DevStudio

DevStudio MCP: Production-Grade Screen Recording Server

Production-grade MCP server for screen recording and demo automation - Phase 1 release featuring 6 professional recording tools with multi-monitor support, audio/video muxing, and seamless integration for AI-driven demo workflows.

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/your-username/devstudio-mcp.git
cd devstudio-mcp

# Install with uv (recommended)
uv sync

# Or with pip
pip install -e .

Note: FFmpeg is bundled with PyAV - no separate installation required! The package includes everything needed for professional video encoding.

Running the Server

# Run the MCP server
devstudio-mcp

# Or with Python
python -m devstudio_mcp.server

🎯 Phase 1 Features

📹 Professional Recording Tools (6 Active Tools)

• Multi-Monitor Recording: Capture any screen in multi-monitor setups
• Audio Capture: Professional audio recording with real-time processing
• Audio/Video Muxing: Automatic combination of separate streams into single MP4 files
• Screenshot Tools: Single-shot screen capture with metadata
• Session Management: Track and manage multiple recording sessions
• Terminal Monitoring: Command history and output capture

📊 Tools Overview

🛠️ MCP Tools Reference

📹 Recording Tools

start_recording

Start a new recording session with screen, audio, and/or terminal capture.

{
    "include_screen": true,
    "include_audio": true,
    "include_terminal": false,
    "output_format": "mp4",
    "screen_id": 1,
    "auto_mux": true,
    "cleanup_source_files": false
}

• Returns: Session ID, status, output directory, recording types
• Use case: Begin capturing screen demos, tutorials, or presentations
• Features:
  • Multi-monitor support (select specific screen with screen_id)
  • Auto-mux: Automatically combine audio+video into single MP4 (default: true)
  • Cleanup: Delete source files after muxing (default: false)
  • Audio-only: Set include_screen=false, include_audio=true

stop_recording

Stop an active recording session and retrieve output file paths.

{
    "session_id": "uuid-of-session"
}

• Returns: File paths for screen/audio/terminal/combined, session duration
• Use case: End recording and get file locations
• Note: When auto_mux=true and both screen+audio recorded, returns combined.mp4 file

capture_screen

Take a single screenshot of any monitor.

{
    "screen_id": 1
}

• Returns: Screenshot file path, dimensions, format, size
• Use case: Quick captures for documentation or bug reports
• Features: Multi-monitor support via screen_id parameter

list_active_sessions

List all active recording sessions and their status.

• Returns: List of active sessions with IDs, start times, types
• Use case: Monitor ongoing recordings or manage multiple sessions

mux_audio_video

Combine separate audio and video files into a single MP4.

{
    "video_path": "/path/to/video.mp4",
    "audio_path": "/path/to/audio.wav",
    "output_path": "/path/to/output.mp4"
}

• Returns: Muxed file path, input files, size
• Use case: Merge separately recorded streams or fix sync issues
• Note: Uses PyAV for professional H.264/AAC encoding

get_available_screens

Get information about all available monitors and their properties.

• Returns: List of screens with ID, resolution, position, scale
• Use case: Select specific monitor for recording in multi-screen setups

🗺️ Roadmap - Development Phases

✅ Phase 1: Recording Infrastructure (Current - v1.0.0)

Status: Production-ready Release: Q4 2024

• ✅ 6 production-ready recording tools
• ✅ Multi-monitor support with screen selection
• ✅ Audio/video muxing (H.264 + AAC)
• ✅ Professional screen capture
• ✅ Session management
• ✅ PyAV integration (bundled FFmpeg)

🚧 Phase 2: AI-Powered Processing (In Development)

Target: Q1 2025 Git Branch: archive/phase-2-3-ai-features

Features:

• 🔜 Audio transcription using OpenAI Whisper
• 🔜 Multi-provider AI (OpenAI, Anthropic, Google)
• 🔜 Content analysis with topic detection
• 🔜 Code extraction from recordings
• 🔜 Automatic chapter detection with timestamps

Tools (3):

• transcribe_audio - Convert audio to text with timestamps
• analyze_content - Extract topics, terms, and structure
• extract_code - Identify and categorize code snippets

📋 Phase 3: Content Generation (Planned)

Target: Q2 2025

Features:

• 📋 AI-generated blog posts from recordings
• 📋 Automatic documentation creation
• 📋 Course outline generation
• 📋 Multi-format content export (Markdown, HTML, PDF)
• 📋 YouTube descriptions with timestamps

Tools (4):

• generate_blog_post - Create technical blog posts
• create_documentation - Generate API/feature docs
• generate_summary - Configurable content summaries
• create_course_outline - Educational content structure

💎 Phase 4: Monetization & Teams (Future)

Target: Q3 2025

Features:

• 💎 Tier-based feature access (Free, Pro, Team, Enterprise)
• 💎 Usage tracking and analytics
• 💎 License management system
• 💎 Team collaboration features
• 💎 Custom branding options

Tools (3):

• get_license_info - Check subscription status
• check_feature_access - Validate feature availability
• get_usage_stats - Monitor usage and quotas

🔮 Vision: Autonomous Demo Recording Platform

DevStudio MCP is evolving into a comprehensive autonomous demo recording platform that combines professional recording infrastructure with AI-driven browser automation.

🌐 The Future: AI-Driven Demo Creation

Imagine: An AI agent that can autonomously create complete product demos by controlling browsers, recording every interaction, and generating documentation - all orchestrated through MCP.

🤖 AWS Nova Act Integration

Autonomous browser control for end-to-end demo creation

DevStudio MCP will integrate with AWS Nova Act agents to enable fully automated demo workflows:

graph LR
    A[MCP: Start Recording] --> B[Nova Act: Browser Automation]
    B --> C[Autonomous Actions]
    C --> D[MCP: Stop Recording]
    D --> E[Complete Demo Video]

Example Workflow: E-commerce Demo

# Start recording via MCP
mcp_client.call_tool("start_recording", {
    "include_screen": true,
    "include_audio": true,
    "auto_mux": true
})

# Launch Nova Act for browser automation
from nova_act import NovaAct
nova = NovaAct(api_key="key")

# Agent performs complete workflow autonomously
nova.act("Visit amazon.com, search for wireless headphones, \
          filter by price under $100, add top rated to cart, \
          proceed to checkout page")

# Stop recording
result = mcp_client.call_tool("stop_recording", {
    "session_id": session_id
})

# Result: Professionally recorded e-commerce demo
print(f"Demo recorded: {result['files']['combined']}")

🎯 Use Cases

1. SaaS Product Demos

Automatically record multi-step user journeys:

• Account creation and onboarding
• Feature exploration workflows
• Integration setup processes
• Admin panel operations

2. Tutorial Creation

Capture complex technical workflows:

• Development environment setup
• API integration examples
• Debugging sessions
• Code refactoring demonstrations

3. QA & Testing

Record test scenarios with browser interaction:

• Automated test execution capture
• Bug reproduction recordings
• Regression testing documentation
• User acceptance test (UAT) videos

4. Customer Onboarding

Create personalized demo content:

• Product walkthroughs
• Feature tutorials
• Best practices guides
• Troubleshooting demonstrations

🔗 MCP Orchestration Architecture

┌─────────────────────────────────────────────┐
│  MCP DevStudio Recording Server (Phase 1)  │
│  • start_recording                          │
│  • capture_screen                           │
│  • stop_recording                           │
│  • multi-monitor support                    │
└──────────────┬──────────────────────────────┘
               │ Orchestrates
               ↓
┌─────────────────────────────────────────────┐
│  AWS Nova Act Browser Automation            │
│  • Navigate websites autonomously           │
│  • Fill forms with natural language         │
│  • Click & interact intelligently           │
│  • Multi-step workflow execution            │
└──────────────┬──────────────────────────────┘
               │
               ↓
┌─────────────────────────────────────────────┐
│  AI Processing Pipeline (Phase 2-3)         │
│  • Transcribe narration                     │
│  • Generate documentation                   │
│  • Create blog posts                        │
│  • Extract code snippets                    │
└─────────────────────────────────────────────┘
               │
               ↓ Complete Demo Package

🚀 User-Defined Environment Integration

Beyond browsers - Record demos across any environment:

• Local Applications: Desktop software demos
• Cloud Platforms: AWS Console, Azure Portal, GCP
• Development Tools: VS Code, IDEs, terminals
• Custom Systems: Internal tools, proprietary software

Nova Act agents can be configured to interact with virtually any web-based interface, making DevStudio MCP the universal recording infrastructure for AI-driven demo creation.

🌟 Why This Matters

Traditional demo creation:

• ⏰ Hours of manual recording and editing
• 🎬 Multiple takes to get it right
• 📝 Manual documentation writing
• 🔄 Constant updates needed

AI-driven with DevStudio MCP + Nova Act:

• ⚡ Automated end-to-end - From browser to video
• 🎯 Consistent quality - Perfect execution every time
• 📚 Auto-documentation - AI-generated content
• 🔁 One-click updates - Re-record entire demos instantly

📖 Usage Examples

Complete Recording Workflow

# 1. Check available monitors
screens = await get_available_screens()
# Returns: [{"id": 0, "width": 1920, "height": 1080, ...}, ...]

# 2. Start recording a tutorial on second monitor
session = await start_recording({
    "include_screen": true,
    "include_audio": true,
    "screen_id": 1,  # Second monitor
    "output_format": "mp4",
    "auto_mux": true
})
# Returns: {"session_id": "...", "status": "recording"}

# 3. Record your demo...

# 4. Take a quick screenshot during recording
screenshot = await capture_screen({"screen_id": 1})
# Returns: {"file_path": "/path/to/screenshot.png", ...}

# 5. Stop recording and get files
result = await stop_recording({
    "session_id": session["session_id"]
})
# Returns: {"files": {"combined": "/path/to/combined.mp4"}, "duration": 120.5}

Multi-Monitor Recording

# Get all available screens
screens = await get_available_screens()
print(f"Found {len(screens)} monitors")

# Record specific monitor
session = await start_recording({
    "include_screen": true,
    "screen_id": 2,  # Third monitor
    "include_audio": true
})

Audio/Video Muxing

# Record audio and video separately
session = await start_recording({
    "include_screen": true,
    "include_audio": true,
    "auto_mux": false  # Don't auto-combine
})

result = await stop_recording({"session_id": session["session_id"]})

# Manually mux later with custom settings
muxed = await mux_audio_video({
    "video_path": result["files"]["screen"],
    "audio_path": result["files"]["audio"],
    "output_path": "/custom/path/final_demo.mp4"
})

🔧 Configuration

Environment Variables

Default Recording Settings

# Default configuration
recording:
  output_dir: ~/DevStudio/recordings
  max_duration: 3600  # 1 hour
  quality: medium
  fps: 30
  audio_enabled: true
  auto_mux: true

🧪 Testing

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=devstudio_mcp

# Test recording tools specifically
uv run pytest tests/test_recording.py -v

🏗️ Architecture

devstudio_mcp/
├── server.py              # Main MCP server (Phase 1)
├── registry.py            # Tool registration (6 tools active)
├── config.py              # Configuration management
├── tools/
│   └── recording.py       # 6 recording tools (ACTIVE)
├── resources/
│   └── media_manager.py   # Media file management
└── utils/
    ├── exceptions.py      # Error handling
    └── logger.py         # Structured logging

Archived for Phase 2/3 (preserved in git branch archive/phase-2-3-ai-features):

• tools/processing.py - AI transcription and analysis (3 tools)
• tools/generation.py - Content generation (4 tools)
• monetization.py - License management (3 tools)
• prompts/ - AI prompt templates
• resources/session_data.py - Session resources

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing feature)
5. Open a Pull Request

📄 License

DevStudio MCP is dual-licensed under AGPL v3 (open source) and Commercial License (proprietary use).

Open Source License (AGPL v3)

Free for:

• ✅ Personal projects and education
• ✅ Open source projects
• ✅ Internal company tools
• ✅ Research and non-commercial use

Requirements:

• ⚠️ If you modify this software, you must release your modifications as AGPL v3
• ⚠️ If you use this in a network service (SaaS), you must open source your entire application

Full AGPL v3 license: | https://www.gnu.org/licenses/agpl-3.0.html

Commercial License

Required for:

• 💼 Commercial SaaS offerings (proprietary)
• 💼 Selling software that includes DevStudio MCP
• 💼 Keeping your modifications proprietary
• 💼 Enterprise deployments without open sourcing

Benefits:

• ✅ No AGPL copyleft requirements
• ✅ Keep your code proprietary
• ✅ Priority support and updates
• ✅ Commercial use indemnification
• ✅ Custom licensing terms available

Pricing:

• Startup: < $1M revenue - Contact for pricing
• Professional: $1M-$10M revenue - Contact for pricing
• Enterprise: > $10M revenue - Custom terms

📧 Contact for commercial licensing: nihitgupta.ng@outlook.com

Full details:

Why Dual License?

We believe in open source while building a sustainable business. AGPL v3 ensures the community benefits from improvements, while commercial licensing allows businesses to use DevStudio MCP in proprietary applications.

🙏 Acknowledgments

• Model Context Protocol - The foundation protocol
• FastMCP - Python MCP framework
• PyAV - FFmpeg bindings with bundled binaries
• AWS Nova Act - Browser automation SDK
• mcpcat.io - MCP best practices and guidelines

📞 Support

• 📧 Email: nihitgupta.ng@outlook.com
• 🐛 Issues: GitHub Issues
• 💼 Commercial Licensing: nihitgupta.ng@outlook.com

Built with ❤️ for developers creating amazing demos and tutorials

Phase 2/3 features (AI processing, content generation, monetization) are actively under development and preserved in git branch: archive/phase-2-3-ai-features