What is Databricks Genie Mcp

databricks-genie-MCP is a Model Context Protocol (MCP) server that connects to the Databricks Genie API, enabling large language models (LLMs) to ask natural language questions, execute SQL queries, and interact with Databricks conversational agents.

Use cases

Use cases include querying data using natural language, generating SQL queries dynamically, facilitating conversational analytics, and enhancing user interaction with data through Databricks Genie.

How to use

To use databricks-genie-MCP, clone the repository, create and activate a virtual environment, install the required dependencies, and set up a .env file with your Databricks host and personal access token. You can then test the server using the inspector or by building and running it with Docker.

Key features

Key features include listing available Genie spaces in your Databricks workspace, fetching metadata for specific Genie spaces, starting new conversations with natural language questions, asking follow-up questions, and retrieving SQL results in a structured format.

Where to use

databricks-genie-MCP can be used in data analytics, business intelligence, and machine learning applications where interaction with Databricks and natural language processing is required.

Databricks Genie MCP Server

A Model Context Protocol (MCP) server that connects to the Databricks Genie API, allowing LLMs to ask natural language questions, run SQL queries, and interact with Databricks conversational agents.

✨ Features

• List Genie spaces available in your Databricks workspace (Currently Manual/Using Resource)
• Fetch metadata (title, description) of a specific Genie space
• Start new Genie conversations with natural language questions
• Ask follow-up questions in ongoing Genie conversations
• Retrieve SQL and result tables in structured format

🧱 Prerequisites

• Python 3.7+
• Databricks workspace with:
  • Personal access token
  • Genie API enabled
  • Permissions to access Genie spaces and run queries

⚙️ Setup

1. Clone this repository

2. Create and activate a virtual environment (recommended):

 python -m venv .venv
 source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt

Create a .env file in the root directory with the following variables:

DATABRICKS_HOST=your-databricks-instance.cloud.databricks.com # Don't add https
DATABRICKS_TOKEN=your-personal-access-token

📌 Manually Adding Genie Space IDs

Note:
At this time, the Databricks Genie API does not provide a public endpoint to list all available space IDs and titles. (afaik)
As a workaround, you need to manually add the Genie space IDs and their titles in the get_genie_space_id() function in main.py.

🧪 Test the Server

You can test the MCP server using the inspector (optional but recommended):

npx @modelcontextprotocol/inspector python main.py

OR

You can directly build and run docker to test the server

💬 Use with Claude Desktop

Download Claude Desktop

Install Your MCP Server:
From your project directory, run:

mcp install main.py

Once Server Installed

1. Connect in Claude

2. Open Claude Desktop

3. Click Resources → Add Resource

4. Select your Genie MCP Server

5. Start chatting with your data using natural language! 🎯

🧾 Obtaining Databricks Credentials

Host
Your Databricks instance URL (e.g., your-instance.cloud.databricks.com) — do not include https://

Token

1. Go to your Databricks workspace

2. Click your username (top right) → User Settings

3. Under the Developer tab, click Manage under “Access tokens”

4. Generate a new token and copy it

🚀 Running the Server

python main.py

This will start the Genie MCP server over the stdio transport for LLM interaction.

🧰 Available MCP Tools

The following MCP tools are available:

Tool Description

1. get_genie_space_id() List available Genie space IDs and titles
2. get_space_info(space_id: str) Retrieve title and description of a Genie space
3. ask_genie(space_id: str, question: str) Start a new Genie conversation and get results
4. follow_up(space_id: str, conversation_id: str, question: str) Continue an existing Genie conversation

🛠️ Troubleshooting

Common Issues

• Invalid host: Ensure the host does not include https://

• Token error: Make sure your personal access token is valid and has access to Genie

• Timeout: Check if the Genie space is accessible and not idle/expired

• No data returned: Ensure your query is valid for the selected space

🔐 Security Considerations

• Keep your .env file secure and never commit it to version control

• Use minimal scope tokens with expiration whenever possible

• Avoid exposing this server in public-facing environments unless authenticated

Claude Desktop Screenshots