
Custom MCP Server on Databricks Apps
Enables deployment and hosting of custom MCP servers on Databricks Apps platform. Provides a template and deployment methods for creating scalable MCP servers with Databricks authentication.
README
Example - custom MCP server on Databricks Apps
This example shows how to create and launch a custom agent on Databricks Apps.
Please note that this example doesn't use any Databricks SDK, and is independent of the mcp
package in the root dir of this repo.
Prerequisites
- Databricks CLI installed and configured
uv
Local development
- run
uv
sync:
uv sync
- start the server locally. Changes will trigger a reload:
uv run custom-server
This will start the server on http://localhost:8000
Deploying a custom MCP server on Databricks Apps
There are two ways to deploy the server on Databricks Apps: using the databricks apps
CLI or using the databricks bundle
CLI. Depending on your preference, you can choose either method.
Both approaches require first configuring Databricks authentication:
export DATABRICKS_CONFIG_PROFILE=<your-profile-name> # e.g. custom-mcp-server
databricks auth login --profile "$DATABRICKS_CONFIG_PROFILE"
Using databricks apps
CLI
To deploy the server using the databricks apps
CLI, follow these steps:
Create a Databricks app to host your MCP server:
databricks apps create mcp-custom-server
Upload the source code to Databricks and deploy the app:
DATABRICKS_USERNAME=$(databricks current-user me | jq -r .userName)
databricks sync . "/Users/$DATABRICKS_USERNAME/my-mcp-server"
databricks apps deploy mcp-custom-server --source-code-path "/Workspace/Users/$DATABRICKS_USERNAME/my-mcp-server"
Using databricks bundle
CLI
To deploy the server using the databricks bundle
CLI, follow these steps
Update the app.yaml
file in this directory to use the following command:
command: ["uvicorn", "custom_server.app:app"]
- In this directory, run the following command to deploy and run the MCP server on Databricks Apps:
uv build --wheel
databricks bundle deploy
databricks bundle run custom-mcp-server
Connecting to the MCP server
To connect to the MCP server, use the Streamable HTTP
transport with the following URL:
https://your-app-url.usually.ends.with.databricksapps.com/mcp/
For authentication, you can use the Bearer
token from your Databricks profile.
You can get the token by running the following command:
databricks auth token -p <name-of-your-profile>
Please note that the URL should end with /mcp/
(including the trailing slash), as this is required for the server to work correctly.
推荐服务器

Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。