Music Collection MCP Server

#!/usr/bin/env python3 """ Music Collection MCP Server - Advanced Analytics Resource This module contains the advanced_analytics_resource implementation. """ from ..mcp_instance import mcp from ..base_handlers import BaseResourceHandler # Import resource implementation - using absolute imports from src.core.resources.advanced_analytics import get_advanced_analytics_markdown class AdvancedAnalyticsResourceHandler(BaseResourceHandler): """Handler for the advanced_analytics resource.""" def __init__(self): super().__init__("advanced_analytics", "1.0.0") def _get_resource_content(self, **kwargs) -> str: """Get advanced analytics in markdown format.""" return get_advanced_analytics_markdown() # Create handler instance _handler = AdvancedAnalyticsResourceHandler() @mcp.resource("collection://analytics") def advanced_analytics_resource() -> str: """ Get advanced collection analytics with comprehensive insights in markdown format. This resource provides deep collection analysis including: - Collection maturity assessment (Beginner to Master levels) - Album type distribution analysis vs. ideal distributions - Edition prevalence and upgrade opportunities - Collection health metrics with multi-factor scoring - Type-specific recommendations for missing albums - Discovery potential and value assessment - Organization recommendations and folder structure analysis - Decade distribution and genre trend analysis - Advanced insights with pattern recognition Returns: Markdown-formatted advanced analytics with: - Collection maturity section with level assessment - Type analysis with distribution vs. ideal ratios - Edition analysis with upgrade recommendations - Health metrics with scoring breakdown - Recommendations section with actionable insights - Value assessment with rarity factors - Discovery section with potential opportunities - Advanced insights with collection patterns - Organization analysis with compliance scoring URI Format: collection://analytics Features: - Maturity level assessment (5 levels from Beginner to Master) - Health scoring with multiple factors - Type distribution vs. ideal analysis - Edition tracking and upgrade suggestions - Value scoring based on rarity - Discovery potential assessment - Pattern recognition and insights - Organization health analysis """ return _handler.get_content()