Host custom MCP servers using Databricks apps

Beta

This feature is in Beta.

Host your own custom or third-party MCP servers as Databricks apps. Custom MCP servers are useful if you already have an MCP server you want to deploy or if you want to run a third-party MCP server as a source of tools.

Requirements

• An MCP server hosted as a Databricks app must implement an HTTP-compatible transport, such as the streamable HTTP transport.

Host an MCP server as a Databricks app

See the custom MCP server repo for an example of writing your own MCP server and deploying it as a Databricks app.

To host an existing Python MCP server as a Databricks app, follow these steps:

Set up your environment

Before deploying your MCP server, authenticate to your workspace using OAuth.

1. Run the following in a local terminal:

   Bash

   databricks auth login --host https://<your-workspace-hostname>

Set up the MCP server

1. Add a requirements.txt to the MCP server's root directory and specify Python dependencies for the server.

   If the server uses uv for package management, add uv to requirements.txt and it handles installing additional dependencies.

2. Add an app.yaml specifying the CLI command to run the MCP server.

   By default, Databricks apps listen on port 8000. If the server listens on a different port, set it using an environment variable override in the app.yaml file.

   Example app.yaml:

   YAML

   command: [
       'uv',
       'run',
       'your-server-name',
       ..., # optionally include additional parameters here
     ]

Deploy the MCP server as a Databricks app

1. Create a Databricks app to host the MCP server:

   Bash

   databricks apps create mcp-my-custom-server

2. Upload the source code to Databricks and deploy the app by running the following commands from the directory containing your app.yaml file:

   Bash

   DATABRICKS_USERNAME=$(databricks current-user me | jq -r .userName)
   databricks sync . "/Users/$DATABRICKS_USERNAME/mcp-my-custom-server"
   databricks apps deploy mcp-my-custom-server --source-code-path "/Workspace/Users/$DATABRICKS_USERNAME/mcp-my-custom-server"

Connect to the custom MCP server

Click the tabs to see how to connect to an MCP server from various environments.

Local environment

Authenticate to your workspace using OAuth as described in Set up your environment.

The following example shows how to connect to the custom MCP server and list available tools:

Python

from databricks_mcp import DatabricksOAuthClientProvider
from databricks.sdk import WorkspaceClient
from mcp.client.session import ClientSession
from mcp.client.streamable_http import streamablehttp_client

databricks_cli_profile = "DEFAULT"
workspace_client = WorkspaceClient(profile=databricks_cli_profile)

# Replace with your actual custom MCP server URL
mcp_server_url = "https://<workspace-hostname>/serving-endpoints/mcp-my-custom-server/invocations"

async def test_connection_to_server():
   async with streamablehttp_client(
      f"{mcp_server_url}", auth=DatabricksOAuthClientProvider(workspace_client)
   ) as (read_stream, write_stream, _), ClientSession(
      read_stream, write_stream
   ) as session:
      # List available tools
      tools = await session.list_tools()
      print(f"Available tools: {[tool.name for tool in tools.tools]}")

Notebook (service principal)

Use a service principal to access the hosted Databricks app in a Databricks notebook.

Python

import json
from databricks.sdk import WorkspaceClient
from databricks_mcp import DatabricksOAuthClientProvider
from databricks.sdk.credentials_provider import ModelServingUserCredentials
from mcp.client.streamable_http import streamablehttp_client as connect
from mcp import ClientSession

async def main():
    app_url = "https://mcp-server.databricksapps.com/mcp"
    client = WorkspaceClient(
      host="https://workspace_url",
      client_id="client_id",
      client_secret="client_secret"
    )

    async with connect(app_url, auth=DatabricksOAuthClientProvider(client)) as (
        read_stream,
        write_stream,
        _,
    ):
        async with ClientSession(read_stream, write_stream) as session:
            await session.initialize()
            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")

await main()

Agent code (on-behalf-of-user)

Set up on-behalf-of-user authorization. See On-behalf-of-user authentication.

The following example shows how to enable on-behalf-of-user access using ModelServingUserCredentials to access the hosted Databricks app from an agent:

Python

import json
from databricks.sdk import WorkspaceClient
from databricks_mcp import DatabricksOAuthClientProvider
from databricks.sdk.credentials_provider import ModelServingUserCredentials
from mcp.client.streamable_http import streamablehttp_client as connect
from mcp import ClientSession

async def main():
    app_url = "https://mcp-server.databricksapps.com/mcp"
    client = WorkspaceClient(credentials_strategy = ModelServingUserCredentials())

    async with connect(app_url, auth=DatabricksOAuthClientProvider(client)) as (
        read_stream,
        write_stream,
        _,
    ):
        async with ClientSession(read_stream, write_stream) as session:
            await session.initialize()
            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")

await main()

Log the agent model using apps.apps scope. See On-behalf-of-user authentication.

Agent code (service principal)

Enable System auth using service principal to access the hosted Databricks app from an agent:

Python

import json
from databricks.sdk import WorkspaceClient
from databricks_mcp import DatabricksOAuthClientProvider
from mcp.client.streamable_http import streamablehttp_client as connect
from mcp import ClientSession

async def main():
    app_url = "https://mcp-server.databricksapps.com/mcp"
    client = WorkspaceClient()

    async with connect(app_url, auth=DatabricksOAuthClientProvider(client)) as (
        read_stream,
        write_stream,
        _,
    ):
        async with ClientSession(read_stream, write_stream) as session:
            await session.initialize()
            tools = await session.list_tools()
            print(f"Available tools: {[tool.name for tool in tools.tools]}")

await main()

Log the agent model using DatabricksApps as a resource. See Automatic authentication passthrough.

Example notebooks: Build an agent with Databricks MCP servers

The following notebooks show how to author LangGraph and OpenAI agents that call MCP tools with custom MCP servers hosted on Databricks apps.

LangGraph MCP tool-calling agent

Open notebook in new tab

OpenAI MCP tool-calling agent

Open notebook in new tab

Additional resources

The apps cookbook provides end-to-end code examples for integrating MCP servers with different frameworks:

• Streamlit MCP integration example
• Dash MCP integration example

For complete source code and additional examples, see the Databricks Apps Cookbook repository.