"Understanding Modal Music" matching MCP servers:

Enables AI agents to safely access and manage a single Apple Music account, including indefinite listening history, analytics, playlists, and recommendations.

Enables structured document understanding of local PDFs via a dual-extract MCP server, combining MinerU text/layout and Qwen3-VL vision with fusion adjudication for field extraction, tables, formulas, and validation. Fully local and offline.

Connects MCP clients to databases like Snowflake, BigQuery, and Databricks through Alkemi's data platform, enabling natural language database queries with proper schema understanding and metadata management for consistent team-wide access.

MCP server for connecting to Microsoft Fabric and Power BI semantic models, enabling workspace browsing, dataset management, TMSL retrieval, DAX queries, model creation/editing, and includes a Best Practice Analyzer with 71 rules.

Enables AI agents and MCP-compliant clients to extract visual data from charts and graphs into structured markdown tables using deterministic, zero-dependency Python.

Provides deterministic tools for understanding, transforming, and verifying structured data via MCP, enabling rule inference from examples and verification of transformed records.

Exposes an agentic AutoML pipeline as a callable tool so any MCP client can upload a CSV and natural language goal and receive a full ML analysis report with SHAP visualizations. Enables autonomous problem understanding, data cleaning, feature engineering, model training, and explainability through a single remote tool call.