Databricks MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Databricks MCP Serverlist tables in the sales catalog"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Databricks MCP Server
A Python MCP server to interact with Databricks — run SQL queries, manage clusters, trigger jobs, run notebooks, explore Unity Catalog metadata, manage secrets, and interact with DBFS.
Setup
1. Install dependencies
pip install -r requirements.txt2. Configure credentials
Edit the .env file:
DATABRICKS_HOST=https://<your-workspace>.azuredatabricks.net
DATABRICKS_TOKEN=<your-access-token>
DATABRICKS_SQL_HTTP_PATH=/sql/1.0/warehouses/<your-warehouse-id>
DATABRICKS_WAREHOUSE_ID=<your-warehouse-id>3. Run the server
python -m databricks_mcp.serverRelated MCP server: Databricks MCP Server
Available Tools (40+)
SQL
Tool | Description |
| Run a SQL query (auto-wakes warehouse) |
| Show execution plan of a SQL query |
Clusters
Tool | Description |
| List all clusters and their states |
| Get current state of a specific cluster |
| Start a cluster |
| Stop a cluster |
| Restart a running cluster |
| Scale cluster workers up/down |
| Poll until cluster is RUNNING |
| Get recent events/errors for a cluster |
Warehouses
Tool | Description |
| List all SQL Warehouses |
| Get detailed status of a warehouse |
| Start a SQL Warehouse |
| Stop a SQL Warehouse |
| Change warehouse size |
Jobs
Tool | Description |
| List all Databricks jobs |
| Create a new job with a notebook task |
| Update a job's name, notebook, or cluster |
| Delete a job |
| Trigger a job run |
| Get status of a job run |
| Cancel a running job run |
| List recent runs of a job |
Notebooks
Tool | Description |
| List notebooks in a workspace path |
| List all objects in a workspace path |
| Create a folder in workspace |
| Run a notebook (fire and forget) |
| Run a notebook and wait for output |
| Export a notebook (HTML/SOURCE/JUPYTER/DBC) |
| Import/upload a notebook to workspace |
| Delete a notebook or folder |
Metadata (Unity Catalog)
Tool | Description |
| List all catalogs |
| List schemas in a catalog |
| List tables in a catalog.schema |
| Get column schema of a table |
| Preview top N rows of a table |
| Create a Delta table from DDL |
| Drop a table |
| Run OPTIMIZE (+ optional ZORDER) |
| Run VACUUM to clean up old files |
Secrets
Tool | Description |
| List all secret scopes |
| List secret keys in a scope (no values) |
DBFS
Tool | Description |
| List files in a DBFS path |
| Delete a file or folder from DBFS |
| Upload a file to DBFS (base64 content) |
| Create a directory in DBFS |
Integrate with Claude Desktop
Add this to your claude_desktop_config.json:
{
"mcpServers": {
"databricks": {
"command": "python",
"args": ["-m", "databricks_mcp.server"],
"cwd": "/path/to/MCP"
}
}
}This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Servers
- Flicense-qualityDmaintenanceEnables AI assistants like Claude to interact with Databricks workspaces through custom prompts and tools. Supports running SQL queries, managing clusters, creating jobs, and accessing workspace resources via the Databricks SDK.2
- Alicense-qualityDmaintenanceEnables LLM-powered tools to interact with Databricks clusters, jobs, notebooks, SQL warehouses, and Unity Catalog through the Model Completion Protocol. Provides comprehensive access to Databricks REST API functionality including cluster management, job execution, workspace operations, and data catalog operations.MIT
- Flicense-qualityDmaintenanceEnables AI agents to access enterprise data from Unity Catalog (vector search, functions, Genie spaces) and perform developer actions in Databricks like managing notebooks and running jobs.
- Alicense-qualityDmaintenanceEnables AI assistants to interact with Databricks workspaces programmatically, providing comprehensive tools for cluster management, notebook operations, job orchestration, Unity Catalog data governance, user management, permissions control, and FinOps cost analytics.263MIT
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