This topic covers platforms and Model Context Protocol (MCP)–based connectors that give AI systems controlled, auditable access to enterprise data for model training, fine-tuning, and retrieval-augmented applications. With increasing regulatory and contractual demands for provenance, consent, and licensing, organizations need solutions that combine data cataloging, lineage, secure connectors, and pipeline orchestration to make datasets usable and auditable for large‑scale model workflows. Recent tooling trends center on MCP servers that expose databases, vector stores, and analytics engines to AI clients while encapsulating connection management, access controls, and semantic views. Examples include Snowflake’s Cortex MCP server (unstructured search, semantic view querying), the MCP Toolbox for Databases (connection pooling and secure tool development), and DBHub (universal gateway for MySQL/MariaDB/Postgres/SQL Server). Specialized servers include MongoDB MCP (Atlas support via service accounts), Neo4j MCP (read/write Cypher and graph-backed memory), MotherDuck’s DuckDB MCP (local + hybrid SQL analytics), Pinecone MCP (vector index read/write for RAG), and mcp-memory-service (production hybrid memory with zero-lock reads and semantic memory search). Key categories—Data Catalog & Lineage, Database Connectors, and Data Pipeline Orchestration—address complementary needs: cataloging and licensing metadata to record provenance and permitted uses; reliable, auditable connectors to enforce access policies; and orchestration to schedule, transform, and log dataset extraction for training. As of 2026, the imperative for reproducibility, legal compliance, and efficient hybrid architectures has made interoperable MCP layers and metadata-driven workflows central to responsible model development. These platforms reduce integration friction, surface licensing constraints, and enable traceable datasets for defensible model training and deployment.