Provides tools for interacting with Databricks, enabling cluster management, job management, notebook management, DBFS management, and SQL management within a Databricks environment.
Databricks MCP Server
This is a server that provides tools to interact with Databricks.
Features
- Cluster management
- Job management
- Notebook management
- DBFS management
- SQL management
Installation
Required Python version: 3.11
Configuration
The server can be configured using environment variables or a .env file.
Environment variables
DATABRICKS_HOST
: The Databricks host URL (e.g. https://adb-123456789012345.12.azuredatabricks.net)DATABRICKS_TOKEN
: The Databricks API tokenTRANSPORT
: The transport to use (e.g. sse). Options: [stdio, sse, streamable-http]. Default: sseSERVER_HOST
: The host to bind the server to (e.g. 0.0.0.0)SERVER_PORT
: The port to bind the server to (e.g. 8000)DEBUG
: Whether to run the server in debug mode (e.g. True)LOG_LEVEL
: The log level (e.g. INFO)
.env file
Create a .env file in the root directory of the project with the following variables:
Obtaining Databricks Credentials
- Host: Your Databricks instance URL (e.g., your-instance.cloud.databricks.com)
- Token: Create a personal access token in Databricks:
- Go to User Settings (click your username in the top right)
- Select "Developer" tab
- Click "Manage" under "Access tokens"
- Generate a new token, and save it (it will not be shown again)
Running the server in standalone mode
Config Claude-Desktop/Cursor/Windsurf
Add the following to your Claude-Desktop/Cursor config file:
If you are using Claude-Desktop, restart it for the changes to take effect.
This server cannot be installed
A server that provides tools to interact with Databricks for cluster, job, notebook, DBFS, and SQL management through natural language interfaces like Claude-Desktop and Cursor.
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