"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.

A read-only MCP server that enables users to query Databricks SQL, browse metadata, and monitor Delta Lake tables. It also supports tracking Databricks Jobs, DLT Pipelines, and cluster metrics through natural language interfaces.

Safe, read-only SQL analytics for AI agents over MCP, enabling exploration, profiling, and querying of data without mutation risk.

A Model Context Protocol server that lets LLM clients answer business questions in natural language over a Databricks dataset without writing SQL by hand.

Enables conversational assessment and migration of Domo estates to Databricks, allowing AI clients to discover, analyze, and transpile Domo assets into Databricks pipelines.

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.

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

Answers natural-language questions against Postgres, Oracle, Snowflake, Databricks, DuckDB and SQLite via db_read(question) and get_schema(), each separately grantable. The model only proposes SQL — shape, access, dialect and query plan are checked deterministically before any rows are read, and the server refuses rather than returning an unverified answer.

Query SQL databases (SQLite, PostgreSQL, BigQuery, Databricks) in natural language through a business semantic layer — glossary, metrics, and a data dictionary grounded against your real schema. Read-only by default, with an embedded SQLite + sqlite-vec metadata store and no external infra required.

Enables AI agents to discover, request access to, and query data products in Data Mesh Manager, enforcing governance policies while retrieving business data from platforms like Snowflake and Databricks.

Enables AI agents to manage Databricks Lakeflow jobs by building and uploading Python wheels and triggering runs with specific arguments. It provides a structured way to orchestrate complex data experiments and monitor execution directly on Databricks clusters.

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.

Enables AI assistants to manage data security on the ALTR platform for Snowflake, Databricks, and OLTP databases, offering 99 tools across 10 domains including tag masking, policies, classification, and access governance.

A Model Context Protocol server implementing a Getting Things Done assistant with tools for tasks, projects, inbox, next actions, and statistics. Supports local SQLite and Databricks deployment.

Celp-MCP enables natural-language analytics over SQL, MongoDB, and Databricks warehouses through MCP-compatible clients, converting questions into multi-step database plans and returning markdown reports.

Provides an MCP-native operational interface for diagnostics, explainability, regression checks, and operational memory on data-system internals like PostgreSQL and Databricks toy engines.