Databricks MCP Server
Enables interaction with Databricks for running SQL queries, managing clusters, triggering jobs, running notebooks, exploring Unity Catalog metadata, managing secrets, and interacting with DBFS.
README
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.txt
2. 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.server
Available Tools (40+)
SQL
| Tool | Description |
|---|---|
sql_query |
Run a SQL query (auto-wakes warehouse) |
sql_explain |
Show execution plan of a SQL query |
Clusters
| Tool | Description |
|---|---|
clusters_list |
List all clusters and their states |
cluster_status |
Get current state of a specific cluster |
cluster_start |
Start a cluster |
cluster_stop |
Stop a cluster |
cluster_restart |
Restart a running cluster |
cluster_resize |
Scale cluster workers up/down |
cluster_wait_until_ready |
Poll until cluster is RUNNING |
cluster_events |
Get recent events/errors for a cluster |
Warehouses
| Tool | Description |
|---|---|
warehouses_list |
List all SQL Warehouses |
warehouse_status |
Get detailed status of a warehouse |
warehouse_start |
Start a SQL Warehouse |
warehouse_stop |
Stop a SQL Warehouse |
warehouse_resize |
Change warehouse size |
Jobs
| Tool | Description |
|---|---|
jobs_list |
List all Databricks jobs |
job_create |
Create a new job with a notebook task |
job_update |
Update a job's name, notebook, or cluster |
job_delete |
Delete a job |
job_trigger |
Trigger a job run |
job_run_status |
Get status of a job run |
job_cancel |
Cancel a running job run |
job_run_history |
List recent runs of a job |
Notebooks
| Tool | Description |
|---|---|
notebooks_list |
List notebooks in a workspace path |
workspace_list |
List all objects in a workspace path |
workspace_mkdir |
Create a folder in workspace |
notebook_run |
Run a notebook (fire and forget) |
notebook_run_and_wait |
Run a notebook and wait for output |
notebook_export |
Export a notebook (HTML/SOURCE/JUPYTER/DBC) |
notebook_import |
Import/upload a notebook to workspace |
notebook_delete |
Delete a notebook or folder |
Metadata (Unity Catalog)
| Tool | Description |
|---|---|
catalogs_list |
List all catalogs |
schemas_list |
List schemas in a catalog |
tables_list |
List tables in a catalog.schema |
table_schema |
Get column schema of a table |
table_preview |
Preview top N rows of a table |
table_create |
Create a Delta table from DDL |
table_drop |
Drop a table |
table_optimize |
Run OPTIMIZE (+ optional ZORDER) |
table_vacuum |
Run VACUUM to clean up old files |
Secrets
| Tool | Description |
|---|---|
secrets_list_scopes |
List all secret scopes |
secrets_list |
List secret keys in a scope (no values) |
DBFS
| Tool | Description |
|---|---|
dbfs_list |
List files in a DBFS path |
dbfs_delete |
Delete a file or folder from DBFS |
dbfs_upload |
Upload a file to DBFS (base64 content) |
dbfs_mkdir |
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"
}
}
}
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.