Databricks MCP Helpers

Warnings

• breaking The library is currently at version `0.x.x` (pre-1.0), meaning API stability is not guaranteed. Breaking changes may be introduced in minor or patch releases without strict semantic versioning.
  Fix: Always pin exact versions in production environments (`databricks-mcp==0.9.0`) and thoroughly test updates before deployment.
• gotcha Authentication requires specific environment variables (`DATABRICKS_HOST`, `DATABRICKS_TOKEN`, `DATABRICKS_ACCOUNT_ID`). A common mistake is using a Databricks Workspace token instead of an Account-level token, which is necessary for Control Plane operations.
  Fix: 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`.
• gotcha `databricks-mcp` is a wrapper around `databricks-sdk`. If `databricks-sdk` introduces breaking changes or significant behavioral shifts that `databricks-mcp` does not immediately accommodate, it can lead to unexpected errors.
  Fix: 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.
• gotcha This library is primarily for Databricks Control Plane (MCP) interactions and complex workflows. For simpler workspace-level operations (e.g., managing notebooks, jobs, clusters within a single workspace), directly using the `databricks-sdk` might be more appropriate and less complex.
  Fix: Evaluate your use case; if it doesn't involve account-level management or complex deployment stacks across workspaces/accounts, the `databricks-sdk` might suffice.

Install

• pip install databricks-mcp Install stable version

Imports

• DatabricksMcpClient

  from databricks_mcp.databricks_mcp_client import DatabricksMcpClient

• DatabricksMcpConfig

  from databricks_mcp.databricks_config import DatabricksMcpConfig

Quickstart

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.

import os
from databricks_mcp.databricks_config import DatabricksMcpConfig
from databricks_mcp.databricks_mcp_client import DatabricksMcpClient

# Ensure DATABRICKS_HOST, DATABRICKS_TOKEN, and DATABRICKS_ACCOUNT_ID
# are set as environment variables for a real connection.
# Token should be a Databricks Account-level token.

try:
    config = DatabricksMcpConfig(
        host=os.environ.get("DATABRICKS_HOST", "https://accounts.cloud.databricks.com"),
        token=os.environ.get("DATABRICKS_TOKEN", "dapi-example-token"),
        account_id=os.environ.get("DATABRICKS_ACCOUNT_ID", "00000000-0000-0000-0000-000000000000"),
    )
    client = DatabricksMcpClient(config)
    print(f"Databricks MCP Client initialized successfully for host: {client.config.host}")
    # The client object contains underlying databricks_sdk clients (e.g., client.account_client)
    # which can be used to perform actual operations. This quickstart only demonstrates initialization.

except Exception as e:
    print(f"Failed to initialize Databricks MCP Client: {e}")
    print("Please ensure DATABRICKS_HOST, DATABRICKS_TOKEN, and DATABRICKS_ACCOUNT_ID")
    print("environment variables are correctly configured and point to your Databricks Account.")