"Databricks" matching MCP servers:

Exposes Databricks REST APIs as MCP tools for managing and querying a Databricks workspace, including clusters, jobs, SQL, Unity Catalog, and more.

Enables AI assistants to interact with Databricks workspaces through natural language, supporting SQL queries, cluster management, jobs, Genie AI, Unity Catalog, and more.

Enables AI agents and assistants to work with a Databricks workspace — SQL, clusters and warehouses, notebooks, Jobs, Lakeflow pipelines, Unity Catalog, Volumes, dashboards, Genie, model serving, Vector Search, Lakebase and Apps — under a safety-first model with read/write/destructive/security classification, confirmation steps for dangerous changes, production resource protection, and a fully read-only mode. Every call is checked against the official Databricks SDK, returns typed schemas and redacted secrets, and unsupported features report an explicit error rather than faking results.

Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.

Triggers OLTP order generation, CDC ETL, and fraud evidence pack queries for the Databricks eCommerce analytics lakehouse.

Enables exposing any Databricks capability as MCP tools for AI agents, allowing MCP clients like Claude, AI Playground, and Copilot Studio to discover and call them without client-side changes.

Enables MCP clients to ask Databricks Genie questions, check Fabric mirroring health, manage Power BI datasets and reports, and create Copilot-ready handoff prompts.