Skip to main content
Glama
Sign In
IRCAM Amplify MCP Server

IRCAM Amplify MCP Server

by pixxelboy
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

IRCAM Amplify MCP Server

MCP (Model Context Protocol) server for IRCAM Amplify audio processing APIs. Enables any MCP-compatible LLM to analyze music, separate stems, detect AI-generated audio, and more.

Features

  • Music Analysis: Extract genre, mood, tempo, key, and instruments from audio

  • Stem Separation: Split audio into vocals, drums, bass, and other instruments

  • AI Detection: Detect whether music is AI-generated or human-made

  • Loudness Analysis: Measure LUFS, true peak, and dynamic range

  • Async Job Handling: Poll long-running operations with progress tracking

Supported Audio Formats

MP3, WAV, FLAC, OGG, M4A (max 100MB)

Quick Start

Prerequisites

Installation

npm install -g ircam-amplify-mcp

Or run directly with npx:

npx ircam-amplify-mcp

Configuration

1. Set your API key

export IRCAM_AMPLIFY_API_KEY="your-api-key-here"

2. Configure your MCP client

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "ircam-amplify": {
      "command": "npx",
      "args": ["ircam-amplify-mcp"],
      "env": {
        "IRCAM_AMPLIFY_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

Tool

Description

Input

Output

analyze_music

Extract genre, mood, tempo, key, instruments

audio_url

{ genre[], mood[], tempo, key, instruments[] }

separate_stems

Split into vocals, drums, bass, other

audio_url

{ vocals_url, drums_url, bass_url, other_url } or { job_id }

detect_ai_music

Detect AI vs human-made music

audio_url

{ confidence, classification }

analyze_loudness

Measure LUFS, peak, dynamic range

audio_url

{ integrated_lufs, true_peak_db, loudness_range }

check_job_status

Poll async operations

job_id

{ status, progress, result }

Usage Examples

Analyze a song

"Analyze this song: https://example.com/song.mp3"

Response:

{
  "genre": ["electronic", "house"],
  "mood": ["energetic", "uplifting"],
  "tempo": 128,
  "key": "A minor",
  "instruments": ["synthesizer", "drums", "bass"]
}

Separate stems

"Separate the vocals from this track: https://example.com/track.mp3"

Response (sync for short files):

{
  "vocals_url": "https://cdn.ircamamplify.io/stems/vocals.wav",
  "drums_url": "https://cdn.ircamamplify.io/stems/drums.wav",
  "bass_url": "https://cdn.ircamamplify.io/stems/bass.wav",
  "other_url": "https://cdn.ircamamplify.io/stems/other.wav"
}

Response (async for longer files):

{
  "job_id": "abc123-def456"
}

Check if AI-generated

"Is this track AI-generated? https://example.com/mystery.mp3"

Response:

{
  "confidence": 85,
  "classification": "ai_generated"
}

Classification values: ai_generated, human_made, or uncertain

Analyze loudness

"Check if this master is ready for Spotify: https://example.com/master.wav"

Response:

{
  "integrated_lufs": -14.0,
  "true_peak_db": -1.0,
  "loudness_range": 6.0
}

Check job status

"Check the status of job abc123-def456"

Response:

{
  "status": "completed",
  "progress": 100,
  "result": {
    "vocals_url": "...",
    "drums_url": "...",
    "bass_url": "...",
    "other_url": "..."
  }
}

Status values: pending, processing, completed, failed

Error Handling

The server provides detailed error messages with actionable suggestions:

Error Code

Meaning

Suggestion

MISSING_API_KEY

API key not configured

Set IRCAM_AMPLIFY_API_KEY environment variable

INVALID_API_KEY

API key rejected

Verify key at app.ircamamplify.io

INVALID_URL

Cannot access audio URL

Ensure URL is publicly accessible

UNSUPPORTED_FORMAT

Audio format not supported

Use MP3, WAV, FLAC, OGG, or M4A

FILE_TOO_LARGE

File exceeds 100MB limit

Use a shorter audio clip

RATE_LIMITED

Too many requests

Wait and retry

JOB_NOT_FOUND

Job ID invalid or expired

Job results expire after 24 hours

Development

# Install dependencies
npm install

# Run in development mode
npm run dev

# Build for production
npm run build

# Run tests
npm test

# Type check
npm run typecheck

# Lint and format
npm run lint
npm run format

Architecture

src/
├── index.ts              # MCP server entry point
├── types/
│   ├── mcp-tools.ts      # MCP tool type definitions
│   └── ircam-api.ts      # IRCAM API response types
├── tools/
│   ├── analyze-music.ts  # Music tagging tool
│   ├── separate-stems.ts # Stem separation tool
│   ├── detect-ai-music.ts # AI detection tool
│   ├── analyze-loudness.ts # Loudness analysis tool
│   └── check-job-status.ts # Job polling tool
└── utils/
    ├── auth.ts           # API key management
    ├── http.ts           # HTTP client with retry
    ├── validation.ts     # Input validation
    └── errors.ts         # Error formatting

License

MIT - See LICENSE for details.

Support

Available Tools

5 tools
analyze_loudnessA

Analyze the loudness of an audio file following EBU R128 standard. Accepts a public URL to an audio file (MP3, WAV, FLAC, OGG, M4A). Returns integrated loudness (LUFS), true peak (dB), and loudness range (LU).

Inputs: audio_url (required).

analyze_musicA

Analyze an audio file to extract genre, mood, tempo, key, and detected instruments. Accepts a public URL to an audio file (MP3, WAV, FLAC, OGG, M4A). Returns structured tags useful for music classification and discovery.

Inputs: audio_url (required).

check_job_statusA

Check the status of an async processing job. Use this to monitor jobs returned by separate_stems and other async operations. Returns job status (pending, processing, completed, failed), progress percentage, and results when completed.

Inputs: job_id (required).

detect_ai_musicA

Detect whether an audio file was generated by AI or created by humans. Accepts a public URL to an audio file (MP3, WAV, FLAC, OGG, M4A). Returns a confidence score (0-100) and classification (ai_generated, human_made, or uncertain).

Inputs: audio_url (required).

separate_stemsA

Separate an audio file into individual stems: vocals, drums, bass, and other instruments. Accepts a public URL to an audio file (MP3, WAV, FLAC, OGG, M4A). For longer files, returns a job_id for async processing - use check_job_status to monitor progress.

Inputs: audio_url (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.2/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: analyze_loudness focuses on loudness metrics, analyze_music on musical features, detect_ai_music on AI detection, separate_stems on audio separation, and check_job_status on job monitoring. The descriptions clearly differentiate their functions, eliminating any ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (e.g., analyze_loudness, check_job_status). This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.

    Tool Count5/5

    With 5 tools, the server is well-scoped for audio analysis and processing. Each tool serves a specific, non-redundant function, and the count is appropriate for covering core operations without being overwhelming or insufficient.

    Completeness4/5

    The tool set covers key audio analysis tasks (loudness, music features, AI detection, stem separation) and includes job status checking for async operations. A minor gap is the lack of tools for audio editing or synthesis, but the provided tools support a complete workflow for analysis and processing within the stated domain.

    Resources

    Related MCP Connectors

    Appeared in Searches