🚀 MCP Databricks
About
My Databricks MCP server to interact with Databricks through LLM models
Details
- Transport
- SSE
Explore
The MCP Databricks server provides a comprehensive toolkit for managing your Databricks environment:
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
🚀 MCP DatabricksCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
🔍 Prerequisites
- 🐍 Python 3.11 or higher
- 💻 A Databricks workspace
- 🔑 Databricks Personal Access Token (PAT)
- 📦 Required Python packages (installed in setup)
2️⃣ Configure environment variables
Create a .env file in the project root with your Databricks credentials:
```ini
The MCP Databricks server provides a comprehensive toolkit for managing your Databricks environment:
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"\ud83d\ude80 mcp databricks": {
"databricks-mcp": {
"command": "docker",
"args": [
"build",
"-t",
"databricks-mcp",
"."
]
}
}
}
}McpServers
{
"databricks-mcp": {
"command": "docker",
"args": [
"build",
"-t",
"databricks-mcp",
"."
]
}
}<div align="center">
</div>
<p align="center">
<b>A powerful Databricks integration for AI assistants via Model Context Protocol</b>
</p>
📖 Introduction
MCP Databricks seamlessly connects AI assistants to your Databricks workspaces through the Model Context Protocol (MCP). Built with Python, it provides a rich collection of tools for managing virtually every aspect of your Databricks environment.
With this server, AI assistants like Claude can:
- 🔧 Manage compute resources with precision
- 📊 Execute SQL queries and analyze results
- 📁 Organize and manipulate workspace objects
- ✨ And much more!
🔍 Prerequisites
- 🐍 Python 3.11 or higher
- 💻 A Databricks workspace
- 🔑 Databricks Personal Access Token (PAT)
- 📦 Required Python packages (installed in setup)
🚀 Quickstart
1️⃣ Clone the repository
git clone https://github.com/leminkhoa/databricks-mcp
cd databricks-mcp
2️⃣ Configure environment variables
Create a .env file in the project root with your Databricks credentials:
```ini
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