This MCP server for Databricks facilitates seamless interaction with Databricks resources through the MCP protocol, supporting efficient LLM integrations. It exposes APIs for cluster management, job execution, notebook operations, and file handling, enabling automation and enhanced data workflows in Databricks environments.
Added on:
Created by:
Mar 22 2025
This MCP server for Databricks facilitates seamless interaction with Databricks resources through the MCP protocol, supporting efficient LLM integrations. It exposes APIs for cluster management, job execution, notebook operations, and file handling, enabling automation and enhanced data workflows in Databricks environments.
Added on:
Created by:
Mar 22 2025
MCP Mirror
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What is Databricks MCP Server?
The Databricks MCP Server allows LLM-powered tools to access and manage Databricks resources through the MCP protocol. It provides functionalities such as listing, creating, terminating, and starting clusters; managing jobs; listing and exporting notebooks; listing files in DBFS; and executing SQL commands. Built with asyncio for efficient asynchronous operation, the server interacts with Databricks REST APIs and exposes tools for automation, scripting, and integration with enterprise workflows. It supports various use cases, including automated cluster management, data pipeline orchestration, and notebook operations, making it a vital tool for Developers, Data Scientists, and DevOps teams working with Databricks.
Who will use Databricks MCP Server?
- Developers
- Data Scientists
- DevOps Engineers
- Data Engineers
- Automation Engineers
How to use the Databricks MCP Server?
- Step 1: Clone the repository from GitHub
- Step 2: Install dependencies and set environment variables
- Step 3: Start the MCP server using the provided scripts
- Step 4: Connect with LLMs or clients supporting MCP protocol
- Step 5: Use the available tools to interact with Databricks resources
Databricks MCP Server's Core Features & Benefits
The Core Features
- list_clusters
- create_cluster
- terminate_cluster
- get_cluster
- start_cluster
- list_jobs
- run_job
- list_notebooks
- export_notebook
- list_files
- execute_sql
The Benefits
- Enables automation of Databricks resource management
- Facilitates integration of LLMs with Databricks
- Supports asynchronous and efficient operations
- Provides comprehensive API access for clusters, jobs, notebooks, and files
Databricks MCP Server's Main Use Cases & Applications
- Automated cluster provisioning and management
- Executing data pipelines and SQL commands
- Notebook management and exporting
- Integrating LLMs with Databricks workflows
FAQs of Databricks MCP Server
Developer
- MCP-Mirror
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