mcp-databricks
Provides tools for interacting with Databricks SQL and metadata, including table discovery, column alterations, and DDL operations.
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., "@mcp-databricksshow me the tables in the default schema"
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.
File Structure
mcp-databricks/
├── .env # Environment variables (DATABRICKS_HOST, TOKEN, etc.)
├── Dockerfile # Containerization for deployment
├── README.md
├── pyproject.toml # or requirement.txt
└── src/
├── __init__.py
├── server.py # Server entrypoint, initializes MCP server and registers tools
├── config.py # Configuration management using Pydantic Settings
├── db/
│ ├── __init__.py
│ ├── client.py # Databricks SQL connection pool / Databricks SDK client
│ └── operations.py # Low-level SQL execution & metadata query logic
├── models/
│ ├── __init__.py
│ ├── metadata.py # Pydantic models for Table/Column schemas and discovery
│ └── operations.py # Pydantic models for Alter/Write request payloads
└── tools/
├── __init__.py
├── metadata.py # MCP tools for reading tables and discovery
└── warehouse.py # MCP tools for altering columns and DDL operations
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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