AI Jam Sessions

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Machine Learning the Old Fashioned Way

An MCP server that teaches AI to play piano and guitar — and sing.
120 songs across 12 genres. Six sound engines. Interactive guitar tablature.
A browser cockpit with vocal synthesizer. A practice journal that remembers everything.

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What is this?

A piano and guitar that AI learns to play. Not a synthesizer, not a MIDI library — a teaching instrument.

An LLM can read and write text, but it can't experience music the way we do. No ears, no fingers, no muscle memory. AI Jam Sessions closes that gap by giving the model senses it can actually use:

• Reading — real MIDI sheet music with deep musical annotations. Not hand-written approximations — parsed, analyzed, and explained.
• Hearing — six audio engines (oscillator piano, sample piano, vocal samples, physical vocal tract, additive vocal synth, physically-modeled guitar) that play through your speakers, so the humans in the room become the AI's ears.
• Seeing — a piano roll that renders what was played as SVG the model can read back and verify. An interactive guitar tablature editor. A browser cockpit with a visual keyboard, dual-mode note editor, and tuning lab.
• Remembering — a practice journal that persists across sessions, so learning compounds over time.
• Singing — vocal tract synthesis with 20 voice presets, from operatic soprano to electronic choir. Sing-along mode with solfege, contour, and syllable narration.

Each of the 12 genres has a richly annotated exemplar — a reference piece the AI studies first, with historical context, bar-by-bar structural analysis, key moments, teaching goals, and performance tips. The other 96 songs are raw MIDI, waiting for the AI to absorb the patterns, play the music, and write its own annotations.

The Piano Roll

The piano roll is how the AI sees music. It renders any song as SVG — blue for right hand, coral for left, with beat grids, dynamics, and measure boundaries:

Für Elise, measures 1–8 — the E5-D#5 trill in blue, bass accompaniment in coral

Two color modes: hand (blue/coral) or pitch-class (chromatic rainbow — every C is red, every F# is cyan). The SVG format means the model can both see the image and read the markup to verify pitch, rhythm, and hand independence.

The Cockpit

A browser-based instrument and vocal studio that opens alongside the MCP server. No plugins, no DAW — just a web page with a piano.

• Dual-mode piano roll — switch between Instrument mode (chromatic pitch-class colors) and Vocal mode (notes colored by vowel shape: /a/ /e/ /i/ /o/ /u/)
• Visual keyboard — two octaves from C4, mapped to your QWERTY keyboard. Click or type.
• 20 voice presets — 15 Kokoro-mapped voices (Aoede, Heart, Jessica, Sky, Eric, Fenrir, Liam, Onyx, Alice, Emma, Isabella, George, Lewis, plus choir and synth-vox), 4 tract-mapped voices, and a synthetic choir section
• 10 instrument presets — the 6 server-side piano voices plus synth-pad, organ, bell, and strings
• Note inspector — click any note to edit velocity, vowel, and breathiness
• 7 tuning systems — Equal temperament, just intonation (major/minor), Pythagorean, quarter-comma meantone, Werckmeister III, or custom cent offsets. Adjustable A4 reference (392–494 Hz).
• Tuning audit — frequency table, interval tester with beat-frequency analysis, and tuning export/import
• Score import/export — serialize the entire score as JSON and load it back
• LLM-facing API — window.__cockpit exposes exportScore(), importScore(), addNote(), play(), stop(), panic(), setMode(), and getScore() so an LLM can compose, arrange, and play back programmatically

The Learning Loop

 Read                 Play                See                 Reflect
┌──────────┐     ┌───────────┐     ┌────────────┐     ┌──────────────┐
│ Study the │     │ Play the  │     │ View the   │     │ Write what   │
│ exemplar  │ ──▶ │ song at   │ ──▶ │ piano roll │ ──▶ │ you learned  │
│ analysis  │     │ any speed │     │ to verify  │     │ in journal   │
└──────────┘     └───────────┘     └────────────┘     └──────┬───────┘
                                                             │
                                                             ▼
                                                    ┌──────────────┐
                                                    │ Next session  │
                                                    │ picks up here │
                                                    └──────────────┘

The Song Library

120 songs across 12 genres, built from real MIDI files. Each genre has one deeply annotated exemplar — with historical context, bar-by-bar harmonic analysis, key moments, teaching goals, and performance tips (including vocal guidance). These exemplars serve as templates: the AI studies one, then annotates the rest.

Genre	Exemplar	Key	What it teaches
Blues	The Thrill Is Gone (B.B. King)	B minor	Minor blues form, call-and-response, playing behind the beat
Classical	Für Elise (Beethoven)	A minor	Rondo form, touch differentiation, pedaling discipline
Film	Comptine d'un autre été (Tiersen)	E minor	Arpeggiated textures, dynamic architecture without harmonic change
Folk	Greensleeves	E minor	3/4 waltz feel, modal mixture, Renaissance vocal style
Jazz	Autumn Leaves (Kosma)	G minor	ii-V-I progressions, guide tones, swing eighths, rootless voicings
Latin	The Girl from Ipanema (Jobim)	F major	Bossa nova rhythm, chromatic modulation, vocal restraint
New-Age	River Flows in You (Yiruma)	A major	I-V-vi-IV recognition, flowing arpeggios, rubato
Pop	Imagine (Lennon)	C major	Arpeggiated accompaniment, restraint, vocal sincerity
Ragtime	The Entertainer (Joplin)	C major	Oom-pah bass, syncopation, multi-strain form, tempo discipline
R&B	Superstition (Stevie Wonder)	Eb minor	16th-note funk, percussive keyboard, ghost notes
Rock	Your Song (Elton John)	Eb major	Piano ballad voice-leading, inversions, conversational singing
Soul	Lean on Me (Bill Withers)	C major	Diatonic melody, gospel accompaniment, call-and-response

Songs progress from raw (MIDI only) → annotated → ready (fully playable with musical language). The AI promotes songs by studying them and writing annotations with annotate_song.

Sound Engines

Six engines, plus a layered combinator that runs any two simultaneously:

Engine	Type	What it sounds like
Oscillator Piano	Additive synthesis	Multi-harmonic piano with hammer noise, inharmonicity, 48-voice polyphony, stereo imaging. Zero dependencies.
Sample Piano	WAV playback	Salamander Grand Piano — 480 samples, 16 velocity layers, 88 keys. The real thing.
Vocal (Sample)	Pitch-shifted samples	Sustained vowel tones with portamento and legato mode.
Vocal Tract	Physical model	Pink Trombone — LF glottal waveform through a 44-cell digital waveguide. Four presets: soprano, alto, tenor, bass.
Vocal Synth	Additive synthesis	15 Kokoro voice presets with formant shaping, breathiness, vibrato. Deterministic (seeded RNG).
Guitar	Additive synthesis	Physically-modeled plucked string — 4 presets (steel dreadnought, nylon classical, jazz archtop, twelve-string), 8 tunings, 17 tunable parameters.
Layered	Combinator	Wraps two engines and dispatches every MIDI event to both — piano+synth, vocal+synth, etc.

Keyboard Voices

Six tunable piano voices, each adjustable per-parameter (brightness, decay, hammer hardness, detune, stereo width, and more):

Voice	Character
Concert Grand	Rich, full, classical
Upright	Warm, intimate, folk
Electric Piano	Silky, jazzy, Fender Rhodes feel
Honky-Tonk	Detuned, ragtime, saloon
Music Box	Crystalline, ethereal
Bright Grand	Cutting, contemporary, pop

Guitar Voices

Four guitar voice presets with physically-modeled string synthesis, each with 17 tunable parameters (brightness, body resonance, pluck position, string damping, and more):

Voice	Character
Steel Dreadnought	Bright, balanced, classic acoustic
Nylon Classical	Warm, soft, rounded
Jazz Archtop	Mellow, woody, clean
Twelve-String	Shimmering, doubled, chorus-like

The Practice Journal

After every session, the server captures what happened — which song, what speed, how many measures, how long. The AI adds its own reflections: what it noticed, what patterns it recognized, what to try next.

---
### 14:32 — Autumn Leaves
**jazz** | intermediate | G minor | 69 BPM × 0.7 | 32/32 measures | 45s

The ii-V-I in bars 5-8 (Cm7-F7-BbMaj7) is the same gravity as the V-i
in The Thrill Is Gone, just in major. Blues and jazz share more than the
genre labels suggest.

Next: try at full speed. Compare the Ipanema bridge modulation with this.
---

One markdown file per day, stored in ~/.ai-jam-sessions/journal/. Human-readable, append-only. Next session, the AI reads its journal and picks up where it left off.

Install

npm install -g ai-jam-sessions

Requires Node.js 18+. No MIDI drivers, no virtual ports, no external software.

Claude Desktop / Claude Code

{
  "mcpServers": {
    "ai_jam_sessions": {
      "command": "npx",
      "args": ["-y", "-p", "ai-jam-sessions", "ai-jam-sessions-mcp"]
    }
  }
}

MCP Tools

41 tools and 3 prompt templates across six categories:

Learn

Tool	What it does
list_songs	Browse by genre, difficulty, or keyword
song_info	Full musical analysis — structure, key moments, teaching goals, style tips
registry_stats	Library-wide stats: total songs, genres, difficulties
list_measures	Every measure's notes, dynamics, and teaching notes
teaching_note	Deep dive into a single measure — fingering, dynamics, context
suggest_song	Recommendation based on genre, difficulty, and what you've played
practice_setup	Recommended speed, mode, voice settings, and CLI command for a song
compare_songs	Cross-genre pattern recognition — key relationships, pitch/interval similarity, shared forms, teaching connections
annotation_progress	Track annotation quality across the library — scores, grades, and improvement suggestions
server_info	Server version, library stats, engine list, active session

Play

Tool	What it does
play_song	Play through speakers — library songs or raw .mid files. Any engine, speed, mode, measure range.
stop_playback	Stop
pause_playback	Pause or resume
set_speed	Change speed mid-playback (0.1×–4.0×)
playback_status	Real-time snapshot: current measure, tempo, speed, keyboard voice, state
view_piano_roll	Render as SVG (hand color or pitch-class chromatic rainbow)
score_performance	Score a MIDI play-along — pitch accuracy, timing, completeness, with graded feedback
mute_hand	Mute or unmute left/right hand during practice — isolate one hand at a time
preview_teaching_cues	See all teaching notes and key moments before playing

Sing

Tool	What it does
sing_along	Singable text — note-names, solfege, contour, or syllables. With or without piano accompaniment.
ai_jam_sessions	Generate a jam brief — chord progression, melody outline, and style hints for reinterpretation

Guitar

Tool	What it does
view_guitar_tab	Render interactive guitar tablature as HTML — click-to-edit, playback cursor, keyboard shortcuts
list_guitar_voices	Available guitar voice presets
list_guitar_tunings	Available guitar tuning systems (standard, drop-D, open G, DADGAD, etc.)
tune_guitar	Adjust any parameter of any guitar voice. Persists across sessions.
get_guitar_config	Current guitar voice config vs factory defaults
reset_guitar	Factory reset a guitar voice

Build

Tool	What it does
add_song	Add a new song as JSON
import_midi	Import a .mid file with metadata
annotate_song	Write musical language for a raw song and promote it to ready
save_practice_note	Journal entry with auto-captured session data
read_practice_journal	Load recent entries for context
list_keyboards	Available keyboard voices
tune_keyboard	Adjust any parameter of any keyboard voice. Persists across sessions.
get_keyboard_config	Current config vs factory defaults
reset_keyboard	Factory reset a keyboard voice
score_annotation	Score annotation quality across 5 dimensions — completeness, depth, specificity, teaching value, vocabulary
validate_song_entry	Validate a song JSON against the schema before adding
transpose_song	Transpose a song up or down by semitones — new key, new notes
list_sections	View structural sections of a song (Intro, Verse, Chorus, etc.)
add_section	Add a section marker to a song for structural navigation

MCP Prompts

Three prompt templates for structured teaching workflows:

Prompt	What it does
annotate_song	Guided annotation workflow — study an exemplar, write musical language for a raw song
practice_plan	Build a structured practice plan based on genre, difficulty, and goals
performance_review	Review a completed session — what went well, what to focus on next

CLI

ai-jam-sessions list [--genre <genre>] [--difficulty <level>]
ai-jam-sessions play <song-id> [--speed <mult>] [--mode <mode>] [--engine <piano|vocal|tract|synth|guitar|piano+synth|guitar+synth>]
ai-jam-sessions sing <song-id> [--with-piano] [--engine <engine>]
ai-jam-sessions view <song-id> [--measures <start-end>] [--out <file.svg>]
ai-jam-sessions view-guitar <song-id> [--measures <start-end>] [--tuning <tuning>]
ai-jam-sessions info <song-id>
ai-jam-sessions tune <keyboard-id> [--param value ...] [--reset] [--show]
ai-jam-sessions tune-guitar <voice-id> [--param value ...] [--reset] [--show]
ai-jam-sessions keyboards
ai-jam-sessions guitars
ai-jam-sessions stats
ai-jam-sessions library
ai-jam-sessions ports
ai-jam-sessions help
ai-jam-sessions --version

Status

v1.4.0. Six sound engines, 41 MCP tools, 3 prompt templates, 120 songs across 12 genres with deeply annotated exemplars. Song transposition, section markers, per-hand mute/solo for focused practice. Interactive guitar tablature editor. Browser cockpit with 20 vocal presets, 10 instrument voices, 7 tuning systems, and an LLM-facing score API. Piano roll visualization in two color modes. Practice journal for persistent learning. Session state persistence across server restarts. MIDI play-along scoring, annotation quality assessment, and cross-genre pattern recognition. The MIDI is all there — the library grows as the AI learns.

Security & Privacy

Data touched: song library (JSON + MIDI), user songs directory (~/.ai-jam-sessions/songs/), guitar tuning configs, practice journal entries, local audio output device.

Data NOT touched: no cloud APIs, no user credentials, no browsing data, no system files outside the user song directory. No telemetry is collected or sent.

Permissions: MCP server uses stdio transport only (no HTTP). CLI accesses local filesystem and audio devices. See SECURITY.md for the full policy.

License

MIT