AI Producer Hub
Provides comprehensive MIDI hardware integration, including device enumeration, performance recording, and AI-assisted content generation from MIDI input.
Supports integration with OpenAI's GPT models for AI-powered music generation and production assistance.
Provides media library management and organization tools.
Enables live streaming and broadcasting with automated content generation and OBS integration.
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., "@AI Producer HubCreate a complete electronic track about cyberpunk cities"
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.
AI Producer Hub
A Model Context Protocol server that integrates AI-powered music generation with professional production tools, enabling autonomous music creation workflows from MIDI input to live streaming.
Overview
The AI Producer Hub bridges traditional MIDI hardware with modern AI music generation, providing a complete production environment that combines:
Real-time MIDI hardware integration
AI-powered vocal and instrumental generation
Professional mixing and mastering tools
Automated DJ mixing and live streaming
Architecture
Components
The hub integrates multiple specialized MCP servers:
Component | Mount Point | Tools | Description |
VirtualDJ-MCP |
| 61 | Professional DJ mixing, stem separation, video integration |
Plex-MCP |
| 15 | Media library management and organization |
SongGeneration-MCP |
| 7 | AI-powered vocal and instrumental generation |
Reaper-MCP |
| 25 | Digital audio workstation and mastering |
OBS-MCP |
| 20 | Live streaming and broadcasting |
Local MIDI Tools | (root) | 8 | Hardware MIDI device integration |
AI Integration
The hub includes advanced AI orchestration capabilities:
SEP-1577 Sampling: Autonomous tool orchestration without client round-trips
Conversational AI: Natural language production guidance
Autonomous Workflows: Complete production pipelines managed by AI agents
Multi-Provider Support: Integration with Anthropic Claude and OpenAI GPT models
MIDI Integration
The hub provides comprehensive MIDI hardware integration with AI-enhanced processing:
Basic MIDI Workflow
# 1. Enumerate connected MIDI devices
list_midi_devices()
# Returns device information for inputs and outputs
# 2. Record MIDI performance
record_midi_performance(device_index=0, duration_seconds=30)
# Captures live performance to timestamped MIDI file
# 3. AI-assisted content generation
midi_to_ai_seed("recording.mid", expansion_style="full_production")
# Analyzes MIDI and generates structured content for AI processing
# 4. Generate complete track with AI vocals
songgen_to_deck(lyrics, genre="Electronic", tempo=128, deck=1)
# Produces professional vocal and instrumental tracksMIDI Tools
Tool | Description |
| Enumerate connected MIDI hardware devices |
| Capture live MIDI performance to file |
| Send MIDI notes to connected devices |
| Play MIDI files through hardware outputs |
| Monitor and debug MIDI data streams |
| Import MIDI data to Reaper DAW |
| Analyze MIDI for AI content generation |
Workflows
AI-Powered Production
Autonomous Track Generation
User: "Create a complete electronic track about cyberpunk cities"
AI: "I'll orchestrate a full production pipeline for 'cyberpunk neon city' theme.
Starting autonomous workflow:
1. Analyzing theme and generating structured lyrics
2. Creating vocal performance with AI synthesis
3. Generating electronic backing tracks and stems
4. Mixing and mastering for professional sound
5. Loading final track to VirtualDJ deck
Progress: Lyrics generated → Vocals rendered → Stems produced → Mix complete → DJ integration finished
Track 'Neon Shadows' is ready on Deck 1. Would you like me to generate variations or start a new track?"Conversational Production Guidance
User: "Help me make this mix more energetic"
AI: "I can analyze your current production and suggest improvements. Looking at the track structure...
Current analysis:
- BPM: 128, Energy level: Medium
- Frequency balance: Mids dominant, highs attenuated
- Dynamic range: Compressed, could use more punch
Suggestions:
1. Increase high-frequency EQ on percussion stems (+3dB at 8kHz)
2. Add sidechain compression on bass to create rhythmic pumping
3. Boost attack on kick drum samples for more impact
4. Layer in additional percussion elements for complexity
Would you like me to implement these changes automatically, or would you prefer to adjust specific elements?"Large-Scale Production Orchestration
User: "I need a 12-track concept album about space exploration"
AI: "I'll orchestrate a complete album production using SEP-1577 sampling across all available tools.
Planning autonomous workflow:
- Album structure: 12 tracks with cohesive theme
- Parallel track generation using SongGeneration-MCP
- Consistent mastering across all tracks via Reaper-MCP
- Automated organization in Plex-MCP library
- Quality analysis and refinement iterations
Estimated completion: 45 minutes
Track list generated → Production pipeline started → Quality checks running
First track 'Launch Sequence' complete. Continuing with remaining tracks..."Automated DJ Sets
ai_dj_set("progressive house summer", num_tracks=6)
# Generates themed track collection with harmonic mixing analysisMIDI-to-Production Pipeline
# 1. Record MIDI performance
record_midi_performance(0, 30)
# 2. AI content analysis and generation
midi_to_ai_seed("recording.mid", "full_production")
# 3. Complete track production
songgen_to_deck(lyrics, genre="Electronic", tempo=128, deck=1)Large-Scale Production
User: "Produce a full album about space exploration"
AI: "I'll manage the complete album production workflow using autonomous orchestration.
Album Concept: 'Cosmic Journey' - 10 tracks exploring space themes
Workflow initiated:
- Generating track concepts and lyrics for each song
- Parallel production using SongGeneration LeVo AI
- Consistent mastering and mixing across all tracks
- Automatic organization into album structure
- Quality analysis and refinement
Status: Track 1/10 'Stellar Winds' → Complete
Track 2/10 'Zero Gravity' → Rendering vocals...
Album completion: ~30 minutes remaining
Would you like me to adjust the production style or add specific elements?"Live Streaming
User: "Start a 2-hour ambient electronic stream on Twitch"
AI: "I'll set up autonomous live streaming with continuous AI-generated content.
Streaming Configuration:
- Theme: Ambient electronic atmospheres
- Duration: 2 hours (120 minutes)
- Platform: Twitch integration via OBS-MCP
- Content strategy: Progressive evolution with smooth transitions
Setup initiated:
- OBS streaming configuration established
- Initial track generation started (3 tracks pre-buffered)
- VirtualDJ automix sequence programmed
- Engagement monitoring activated
Stream Status: Pre-roll tracks loaded → Going live in 30 seconds
Content pipeline: Track generation every 12-15 minutes
Quality monitoring: Active with automatic adjustments
Stream is now live! I'll continue generating fresh content throughout."🚀 Installation
Prerequisites
uv installed (RECOMMENDED)
Python 3.12+
📦 Quick Start
Run immediately via uvx:
uvx ai-producer-hub🎯 Claude Desktop Integration
Add to your claude_desktop_config.json:
"mcpServers": {
"ai-producer-hub": {
"command": "uv",
"args": ["--directory", "D:/Dev/repos/ai-producer-hub", "run", "ai-producer-hub"]
}
}Requirements
Python 3.11 or later
MIDI hardware (optional, for hardware integration)
Component MCP servers (VirtualDJ, Plex, SongGeneration, Reaper, OBS)
🚀 Installation
Prerequisites
uv installed (RECOMMENDED)
Python 3.12+
📦 Quick Start
Run immediately via uvx:
uvx ai-producer-hub🎯 Claude Desktop Integration
Add to your claude_desktop_config.json:
"mcpServers": {
"ai-producer-hub": {
"command": "uv",
"args": ["--directory", "D:/Dev/repos/ai-producer-hub", "run", "ai-producer-hub"]
}
}Component Servers
Install the required MCP server components:
# Install component servers (adjust paths as needed)
uv pip install -e /path/to/virtualdj-mcp
uv pip install -e /path/to/plex-mcp
uv pip install -e /path/to/songgeneration-mcp
uv pip install -e /path/to/reaper-mcp
uv pip install -e /path/to/obs-mcpMCP Server Configuration
Add to your MCP client configuration:
{
"mcpServers": {
"ai-producer-hub": {
"command": "python",
"args": ["-m", "ai_producer_hub"],
"cwd": "/path/to/ai-producer-hub"
}
}
}Optional Dependencies
For full AI functionality:
pip install ai-producer-hub[ai]For development:
pip install ai-producer-hub[dev]For documentation:
pip install ai-producer-hub[docs]Usage Examples
Basic MIDI Workflow
User: "Check my MIDI devices and record a 30-second performance"
AI: "Found MIDI devices: MPK mini 3 (input), Microsoft GS (output)
Starting 30-second recording... Play your performance now.
Recording complete! Saved as 'midi_performance_20241201_143022.mid'
Analyzing MIDI content for AI processing..."Autonomous Production
User: "Create a complete electronic track about cyberpunk cities"
AI: "Initiating autonomous production pipeline for 'cyberpunk city night' theme.
Production stages:
1. Theme analysis and lyric structure generation
2. Vocal synthesis using SongGeneration LeVo AI
3. Electronic backing track composition
4. Professional mixing and mastering
5. VirtualDJ deck integration
Status: Lyrics generated → Vocals rendering → Stems produced → Mix complete
Track 'Neon Metropolis' loaded to Deck 1. BPM: 130, Key: F minor.
Would you like me to generate variations or start mixing?"Large-Scale Projects
User: "Make me a 12-track album about urban exploration"
AI: "Planning autonomous album production for 'Urban Exploration' concept.
Album structure:
- Track 1: Street Lights - Opening ambient
- Track 2: Concrete Dreams - Driving rhythm
- Track 3: Hidden Alleys - Atmospheric interlude
- ...continuing through 12 tracks
Starting parallel production pipeline using SEP-1577 sampling:
- SongGeneration-MCP: Vocal and instrumental generation
- Reaper-MCP: Mastering and final production
- Plex-MCP: Library organization
Progress: 3/12 tracks complete. Estimated completion: 42 minutes.
First tracks available in your Plex music library."Conversational Assistance
User: "The mix needs more energy in the drop sections"
AI: "Analyzing your current production mix...
Current assessment:
- Energy profile: Moderate, could benefit from increased dynamics
- Frequency response: Mids-heavy, highs attenuated
- Rhythmic elements: Steady but lacking impact
Recommendations:
1. Apply upward compression to percussion bus (+4dB threshold, 3:1 ratio)
2. Add high-frequency boost to cymbals (2-3dB at 12kHz)
3. Implement sidechain ducking on pads during drops
4. Layer additional percussion hits for build-up tension
I can implement these changes automatically, or guide you through manual adjustments. Which approach would you prefer?"Architecture
ai-producer-hub/
├── AI Integration Layer
│ ├── ai_integration.py # SEP-1577 sampling + conversational AI
│ └── Autonomous workflows # AI-managed production pipelines
│
├── Local MIDI Tools (8 tools)
│ ├── Device enumeration # python-rtmidi + mido
│ ├── Performance capture # Live recording and processing
│ ├── Hardware control # MIDI I/O and monitoring
│ └── AI content analysis # MIDI-to-content conversion
│
├── Component MCP Servers
│ ├── /dj/* - VirtualDJ-MCP (61 tools) - Professional mixing
│ ├── /plex/* - Plex-MCP (15 tools) - Media library management
│ ├── /songgen/* - SongGeneration-MCP (7 tools) - AI vocal/instrumental generation
│ ├── /reaper/* - Reaper-MCP (25 tools) - DAW and mastering
│ └── /obs/* - OBS-MCP (20 tools) - Live streaming
│
└── Cross-Server Orchestration
├── ai_produce_track() - Autonomous track production
├── ai_orchestrate_production() - SEP-1577 sampling workflows
├── ai_collaborate_workflow() - Conversational AI assistance
├── ai_stream_production() - Automated live streaming
└── ai_analyze_production() - AI-powered quality assessmentDevelopment
Requirements
Python 3.11+
FastMCP 3.1.0+
Component MCP servers (see Installation)
Development Setup
# Install development dependencies
uv pip install -e .[dev,ai,docs]
# Run tests
pytest tests/ -v --cov=src
# Lint and format
ruff check . --fix
ruff format .
# Type checking
mypy src/Project Structure
src/ai_producer_hub/- Main packagemcpb/- MCPB packaging configuration.zed/- Zed editor integration.github/workflows/- CI/CD pipelinestests/- Test suite
🛡️ Industrial Quality Stack
This project adheres to SOTA 14.1 industrial standards for high-fidelity agentic orchestration:
Python (Core): Ruff for linting and formatting. Zero-tolerance for
printstatements in core handlers (T201).Webapp (UI): Biome for sub-millisecond linting. Strict
noConsoleLogenforcement.Protocol Compliance: Hardened
stdout/stderrisolation to ensure crash-resistant JSON-RPC communication.Automation: Justfile recipes for all fleet operations (
just lint,just fix,just dev).Security: Automated audits via
banditandsafety.
License
MIT License
Contributing
Contributions are welcome. Please see the development documentation for guidelines.
Related Projects
SongGeneration-MCP - AI music generation
VirtualDJ-MCP - Professional DJ mixing
Plex-MCP - Media library management
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