AI Producer Hub
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| songgen_to_deckB | Generate an AI track using LeVo model and load it to VirtualDJ. This is the core AI Producer workflow with state-of-the-art AI:
|
| ai_dj_setB | Generate a complete AI DJ set from a theme. This workflow:
|
| remix_plex_trackA | Take a track from Plex library and create an AI remix. Workflow:
|
| bpm_bridge_generatorA | Generate a transition track that bridges between two BPMs. Perfect for smooth genre transitions in a DJ set. Generates a track that starts at one BPM and gradually transitions to another. |
| live_stream_producerB | Run a live streaming DJ set with AI-generated music. The ultimate AI producer workflow:
|
| album_factoryA | Generate a complete album from a theme. This workflow:
|
| karaoke_generatorA | Generate a karaoke track from lyrics. Workflow:
|
| ai_mashupA | Create an AI-powered mashup of two tracks. Workflow:
|
| hub_statusB | Get status of all mounted servers and available workflows. |
| producer_helpB | Get help for AI Producer Hub workflows. |
| ai_produce_trackC | AI-driven complete track production with sampling capabilities. This tool demonstrates SEP-1577 by autonomously orchestrating the entire music production pipeline using sampling with tools. |
| ai_orchestrate_productionC | SEP-1577 Sampling Implementation: AI autonomously decides tool usage and sequencing. This demonstrates the core sampling capability where the LLM can autonomously orchestrate complex workflows without client round-trips. |
| ai_colaborate_workflowC | Conversational tool returns for natural AI collaboration. This enables ongoing conversations about production workflows with context awareness and natural language responses. |
| ai_analyze_productionC | AI-powered analysis of production progress and quality. |
| ai_stream_productionC | Autonomous live streaming production with AI-generated content. |
| list_midi_devicesA | List all available MIDI input and output devices. Scans the system for connected MIDI devices including:
Returns: Dict with lists of input and output devices Example output: { "inputs": ["MPK mini 3", "loopMIDI Port"], "outputs": ["Microsoft GS Wavetable Synth", "loopMIDI Port"] } |
| record_midi_performanceB | Record a MIDI performance from an input device. Captures all MIDI events (notes, control changes, pitch bend) from the specified device for the given duration. |
| send_midi_noteC | Send a MIDI note to an output device. |
| midi_to_reaperB | Import a MIDI file into Reaper DAW. Sends the MIDI file to Reaper for:
|
| midi_to_ai_seedA | Use a MIDI recording as a seed for AI music generation. Takes your played melody/chords and:
|
| play_midi_fileC | Play a MIDI file through an output device. Sends the MIDI file to your synthesizer/sound module for real-time playback. |
| midi_monitorA | Monitor MIDI input and display incoming messages. Useful for:
|
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 22 tools
The MIDI tools (record_midi_performance, send_midi_note, play_midi_file, midi_monitor, list_midi_devices) are clearly distinct, but the AI workflow tools overlap heavily: ai_produce_track, ai_orchestrate_production, and ai_colaborate_workflow all describe autonomous AI orchestration, and live_stream_producer vs ai_stream_production appear to do nearly the same thing. Descriptions help somewhat but boundaries between the AI-orchestration tools are genuinely fuzzy.
All names use snake_case, which is consistent, and many follow a verb_noun pattern (send_midi_note, play_midi_file, list_midi_devices). There are deviations (ai_* prefixed tools, noun-style album_factory/karaoke_generator/hub_status) and a typo (ai_colaborate_workflow), but the surface remains readable and predictable.
At 22 tools this sits in the heavy range for a server whose purpose is one production pipeline, and several tools (multiple AI-orchestration and streaming variants) feel redundant rather than each earning its place. It is not extreme, but it leans toward over-provisioned.
The domain (AI music production and DJ mixing) is covered broadly: MIDI I/O, track/album generation, remix, mashup, karaoke, streaming, and library integration. Minor gaps exist around explicit track editing/mixing controls, but core lifecycle workflows are represented.