Databricks MCP Python API

Setup:

Install databricks-mcp.

pip install -U databricks-mcp

class databricks_mcp.DatabricksOAuthClientProvider(workspace_client: WorkspaceClient)

Bases: OAuthClientProvider

An OAuthClientProvider for Databricks. This class extends mcp.client.auth.OAuthClientProvider and can be used with the mcp.client.streamable_http to authorize the MCP Server with Databricks.

Usage:

from databricks_mcp.oauth_provider import DatabricksOAuthClientProvider
from mcp.client.streamable_http import streamablehttp_client
from mcp.client.session import ClientSession

# Initialize the Databricks workspace client
workspace_client = WorkspaceClient()

async with streamablehttp_client(
    url="https://mcp-server-url",
    auth=DatabricksOAuthClientProvider(workspace_client),
) as (read_stream, write_stream, _):
    async with ClientSession(read_stream, write_stream) as session:
        await session.initialize()

Parameters:: workspace_client (databricks.sdk.WorkspaceClient) – The Databricks workspace client used for authentication and requests.

class databricks_mcp.DatabricksMCPClient(server_url: str, workspace_client: WorkspaceClient | None = None)

Bases: object

Note

Experimental: This class may change or be removed in a future release without warning.

A client for interacting with a MCP(Model Context Protocol) servers on Databricks.

This class provides a simplified interface to communicate with a specified MCP server URL with Databricks Authorization. Additionally this client provides helpers to retrieve the dependent resources for Databricks Managed MCP Resources to enable automatic authorization in Model Serving.

server_url

The base URL of the MCP server to which this client connects.

Type:: str

client

The Databricks workspace client used for authentication and requests.

Type:: databricks.sdk.WorkspaceClient

list_tools() → List[Tool]

Lists the tools for the current MCP Server. This method uses the streamablehttp_client from mcp to fetch all the tools from the MCP server.

Returns:: A list of tools for the current MCP Server.
Return type:: List[mcp.types.Tool]

call_tool(tool_name: str, arguments: dict[str, Any] | None = None) → CallToolResult

Calls the tool with the given name and input. This method uses the streamablehttp_client from mcp to call the tool.

Parameters:

tool_name (str) – The name of the tool to call.
arguments (dict[str, Any], optional) – The arguments to pass to the tool. Defaults to None.

Returns:

The result of the tool call.

Return type:

mcp.types.CallToolResult

get_databricks_resources() → List[DatabricksResource]

Returns a list of dependent Databricks resources for the current MCP server URL.

If authoring a custom code agent that runs tools from a Databricks Managed MCP server, call this method and pass the returned resources to mlflow.pyfunc.log_model when logging your agent, to enable your agent to authenticate to the MCP server and run tools when deployed.

Note that this method only supports detecting resources for Databricks-managed MCP servers. For custom MCP servers or other MCP server URLs, this method returns an empty list