Enables AI agents to query live schema, lineage, and query-context across data warehouses, dbt projects, orchestration systems, and BI tools via MCP tools.
Enables natural language querying of Apache Iceberg lakehouse by exposing typed tools for namespace discovery, table metadata inspection, snapshot history, time travel SQL generation, and partition pruning explanation.
Enables AI-powered MCP clients to interact with data lakehouse components including Kafka, Flink, and Trino/Iceberg for managing topics, jobs, catalogs, and executing queries.
Exposes Azure AI Foundry agents, workflows, and AI Search vector-database capabilities as MCP tools, enabling natural language interaction with agents, semantic search, and index management.
Turns warehouse/lakehouse tables into a governed entity-relationship knowledge graph exposed through MCP, enabling AI agents to answer multi-table business questions without hard-coded SQL or large schema prompts.