Find BGM MCP Server

An MCP server that helps YouTube content creators find perfect background music for their shorts by analyzing script content and recommending tracks from YouTube Music.

Features

• Script Analysis: Analyzes mood, theme, pacing, and sentiment from video scripts

• Smart Recommendations: Uses YouTube Music API to find suitable background tracks

• Duration Filtering: Ensures recommendations fit your short video length

• Confidence Scoring: Ranks recommendations by relevance to your content

Related MCP server: Sonicmatch

Architecture

The server follows clean architecture principles with modular design:

find_bgm/
├── server.py              # Main server entry point
├── config.py              # Configuration management
├── models.py              # Data models and types
├── script_analyzer.py     # Script analysis logic
├── music_service.py       # YouTube Music API integration
├── tools.py               # MCP tool definitions
└── test_server.py         # Test suite

Installation

1. Install dependencies:

pip install -r requirements.txt

2. (Optional) Set up YouTube Music API access:

   • Follow the ytmusicapi setup guide

   • Create oauth.json file in the project directory

   • Without this, the server will use mock recommendations

Usage

The server provides one main tool: recommend_background_music

Parameters

• script (required): Your YouTube short script/content

• duration (required): Length of your short in seconds (15-60)

• genre_preference (optional): "pop", "electronic", "chill", "rock", "hip-hop", "classical", "ambient", "any"

• mood_preference (optional): "upbeat", "calm", "dramatic", "energetic", "relaxed", "motivational", "any"

• content_type (optional): "comedy", "educational", "lifestyle", "fitness", "cooking", "travel", "tech", "other"

Example Response

{
  "analysis": {
    "detected_mood": "motivational",
    "detected_theme": "fitness", 
    "pacing": "medium",
    "sentiment_score": 0.4,
    "keywords": ["workout", "energy", "strong"]
  },
  "recommendations": [
    {
      "title": "Uplifting Corporate Background",
      "artist": "Audio Library",
      "youtube_music_id": "abc123",
      "confidence_score": 0.85,
      "reason": "Strong match for motivational mood and fitness content",
      "duration": 45,
      "loop_suitable": true
    }
  ]
}

Configuration

Customize behavior with environment variables:

# Logging level
export BGM_LOG_LEVEL=DEBUG

# OAuth file location
export BGM_OAUTH_FILE=my_oauth.json

# Search and recommendation limits
export BGM_MAX_DURATION=240
export BGM_SEARCH_LIMIT=15

YouTube Music API Setup

Method 1: Browser Authentication (Recommended)

1. Install ytmusicapi: pip install ytmusicapi

2. Run: ytmusicapi browser

3. Follow prompts to paste browser headers from YouTube Music

4. Save as oauth.json

Method 2: OAuth Setup

1. Create Google Cloud project

2. Enable YouTube Data API v3

3. Create OAuth credentials

4. Run: ytmusicapi oauth

5. Complete authentication flow

Without the API, the server works with mock data for testing.

Running the Server

python server.py

The server runs on stdio and can be integrated with any MCP-compatible client.

Testing

# Test all components
python test_server.py

# Test with virtual environment
source venv/bin/activate
python test_server.py

Claude Desktop Integration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "find-bgm": {
      "command": "/path/to/find_bgm/venv/bin/python",
      "args": ["/path/to/find_bgm/server.py"]
    }
  }
}

Components

ScriptAnalyzer

Analyzes script content to detect mood, theme, and pacing using natural language processing.

YouTubeMusicService & MusicRecommendationService

Handles YouTube Music API integration and generates scored recommendations.

BGMTools

MCP tool interface that orchestrates script analysis and music recommendations.

Configuration Management

Environment-based configuration with sensible defaults and type safety.

Example Usage

from models import RecommendationRequest
from script_analyzer import ScriptAnalyzer
from music_service import MusicRecommendationService

# Analyze script
analyzer = ScriptAnalyzer()
analysis = analyzer.analyze_script("Your video script here")

# Get recommendations
service = MusicRecommendationService(music_service, config)
recommendations = await service.get_recommendations(
    analysis, "electronic", "upbeat", 30
)

The server provides intelligent music recommendations to help creators find the perfect soundtrack for their content! 🎵