Databricks MCP Server
what is Databricks MCP Server?
Databricks MCP Server is a Model Context Protocol (MCP) server that connects to the Databricks API, enabling users to run SQL queries, list jobs, and check job statuses within Databricks.
how to use Databricks MCP Server?
To use the Databricks MCP Server, clone the repository, set up a virtual environment, install dependencies, configure your Databricks credentials in a .env file, and run the server using the command python main.py.
key features of Databricks MCP Server?
- Execute SQL queries on Databricks SQL warehouses
- List all jobs in Databricks
- Retrieve the status of specific jobs
- Access detailed information about jobs
use cases of Databricks MCP Server?
- Running SQL queries to analyze data in Databricks.
- Monitoring job statuses for data processing tasks.
- Integrating with LLMs for natural language queries about data and jobs.
FAQ from Databricks MCP Server?
- What are the prerequisites to use the server?
You need Python 3.7+, a Databricks workspace with a personal access token, and permissions to run queries.
- How do I obtain Databricks credentials?
You can create a personal access token in Databricks under User Settings, and find your SQL warehouse HTTP path in the SQL Warehouses section.
- Is there a way to test the connection?
Yes, you can run the included test script
python test_connection.pyto verify your connection.
Server Config
{
"mcpServers": {
"databricks": {
"command": "python3",
"args": [
"main.py"
]
}
}
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