MCP Server for Databricks

A Model Completion Protocol (MCP) server for interacting with Databricks services

Model Context Protocol (MCP) server for Databricks that empowers AI agents to autonomously interact with Unity Catalog metadata. Enables data discovery, lineage analysis, and intelligent SQL execution. Agents explore catalogs/schemas/tables, understand relationships, discover notebooks/jobs, and execute queries - greatly reducing ad-hoc query time.

Create your custom MCP server on Databricks Apps for tools, prompts, and claude slash commands

This project implements a Model Context Protocol (MCP) server that exposes Databricks Genie API capabilities as tools

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Host the [AI Dev Kit](https://github.com/databricks-solutions/ai-dev-kit) MCP server as a Databricks App — letting you experience 80+ Databricks tools from the AI Playground, no local setup required.

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Building a Supply-Chain Copilot with OpenAI Agent SDK and Databricks MCP Servers

Databricks MCP Server

MCP server for Databricks

Databricks SQL MCP Server

263 tools for Databricks via MCP. SDK-first, covers Unity Catalog, SQL, Compute, Jobs, Serving, Vector Search, Apps, Lakebase, and more.

This read-only MCP Server allows you to connect to Databricks data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

A MCP server generation framework for databricks apps and unity catalog

This is a Databricks App that implements a chatbot using Genie and Visualizations thru managed Databricks MCP Servers

Production-grade MCP server for Databricks: SQL Warehouses, Jobs API, multi-workspace support.

A powerful Model Context Protocol (MCP) server for executing Databricks SQL queries and comparing table data.

This project demonstrates how to create an Agent in Databricks using Managed MCP (Model Context Protocol) Server for Genie.

MCP Server + skills e agentes Claude Code para Databricks. Execute SQL, explore o Unity Catalog, analise tabelas, crie notebooks PySpark e gerencie MLflow, tudo pelo terminal.

This application takes input from user via PDF and databricks table, updates vector index, calls MCP server for internal policies and ask LLM to answer in case the policy is not present in MCP server. Application creates monitoring logs and provide precision and MRR of the retrieval.

MCP server for managing Databricks permissions and credentials

Databricks MCP server

databricks-mcp-server

databricks mcp server

databricks-mcp-server

Databricks MCP Server forked from https://github.com/RafaelCartenet/mcp-databricks-server

Databricks MCP Server analysis

MCP for Databricks Server