The `databricks-mcp` library provides helper utilities specifically designed for developers interacting with Databricks Workflows and the Databricks Control Plane (MCP). It wraps and extends the `databricks-sdk` to offer higher-level abstractions for common MCP operations, such as deployment stack management. Currently at version `0.9.0`, it is actively developed by DatabricksLabs, with releases tied to internal needs and `databricks-sdk` updates, typically on an infrequent basis.
pip install databricks-mcpVerified import paths — ran on the pinned version, not inferred.
Initialize the `DatabricksMcpClient` by providing configuration, typically sourced from environment variables. This example demonstrates basic client instantiation, which validates your host and token against expected formats but doesn't make an external API call by default unless you interact with `client.account_client` or similar.
Always pin exact versions in production environments (`databricks-mcp==0.9.0`) and thoroughly test updates before deployment.
Ensure `DATABRICKS_TOKEN` is an Account-level token obtained from your Databricks Account settings and that all three environment variables are correctly set before initializing `DatabricksMcpConfig`.
Keep `databricks-mcp` and `databricks-sdk` versions synchronized and regularly check the release notes for both libraries. Consider pinning specific versions of `databricks-sdk` if stability is critical.
Evaluate your use case; if it doesn't involve account-level management or complex deployment stacks across workspaces/accounts, the `databricks-sdk` might suffice.