Install skills for your AI client (downloads from remote)

February 3, 2026 · View on GitHub

Databricks MCP Code Execution Template

This template enables AI-assisted development in Databricks by leveraging the Databricks Command Execution API through an MCP server. Test code directly on clusters, then deploy with Databricks Asset Bundles (DABs).

🎯 What This Does

✅ Run and test code directly on Databricks clusters
✅ Auto-select clusters - no need to specify a cluster ID
✅ Create and deploy Databricks Asset Bundles (DABs)
✅ All from natural language prompts!

Just describe what you want → AI builds, tests the code on Databricks, and deploys the complete pipeline.

🚀 Quick Start (Recommended Workflow)

Step 1: Set Up the MCP Server (One Time)

Clone and set up the MCP server somewhere on your machine:

git clone https://github.com/databricks-solutions/databricks-exec-code-mcp.git
cd databricks-exec-code-mcp
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Step 2: Configure Databricks Credentials

Add to your ~/.zshrc or ~/.bashrc:

export DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
export DATABRICKS_TOKEN=dapi_your_token_here

Make sure the variables are loaded:

source ~/.zshrc

To get your Personal Access Token (PAT): Databricks workspace → Profile → Settings → Developer → Access Tokens → Generate new token

Step 3: Start a New Project

Create your project directory and install the Databricks skills:

# Create and enter your project
mkdir my-databricks-project && cd my-databricks-project

# Initialize git in your my-databricks-project project
git init .

# Install skills for your AI client (downloads from remote)
curl -sSL https://raw.githubusercontent.com/databricks-solutions/databricks-exec-code-mcp/main/install_skills.sh | bash -s -- --cursor
# Or for Claude Code:
curl -sSL https://raw.githubusercontent.com/databricks-solutions/databricks-exec-code-mcp/main/install_skills.sh | bash -s -- --claude
# Or for both:
curl -sSL https://raw.githubusercontent.com/databricks-solutions/databricks-exec-code-mcp/main/install_skills.sh | bash -s -- --all

This creates:

Cursor: .cursor/rules/ with Databricks rules
Claude Code: .claude/skills/ with Databricks skills

Step 4: Configure Your AI Client

Point your AI client to the MCP server you set up in Step 1.

For Cursor — create .cursor/mcp.json in your project:

{
  "mcpServers": {
    "databricks": {
      "command": "/path/to/databricks-exec-code-mcp/.venv/bin/python",
      "args": ["/path/to/databricks-exec-code-mcp/mcp_tools/tools.py"]
    }
  }
}

For Claude Code — run in your project:

claude mcp add-json databricks '{"command":"/path/to/databricks-exec-code-mcp/.venv/bin/python","args":["/path/to/databricks-exec-code-mcp/mcp_tools/tools.py"]}'

Replace /path/to/databricks-exec-code-mcp with the actual path from Step 1.

Step 5: Start Prompting!

💡 Smart Cluster Selection: If no cluster_id is provided, the MCP server automatically finds a running cluster in your workspace.

Just describe what you want in natural language:

Data Engineering:

"Build a Data Engineering pipeline using Medallion Architecture on the NYC Taxi dataset and deploy it with DABs"

Machine Learning:

"Train a classification model on the Titanic dataset, register it to Unity Catalog, and deploy as a DAB job"

Quick Test:

"Run a SQL query to show the top 10 tables in my catalog"

📁 What Gets Generated

The AI will create a complete DABs project:

your-project/
├── databricks.yml              # DABs configuration
├── resources/
│   └── training_job.yml        # Databricks job definition
├── src/<project>/
│   └── notebooks/
│       ├── 01_data_prep.py
│       ├── 02_training.py
│       └── 03_validation.py
└── tests/                      # Unit tests (optional)

🌟 Features

Feature	Description
Direct Cluster Execution	Test code on Databricks clusters via Databricks Execution API
DABs Packaging	Production-ready bundle deployment
Multi-Environment	Support for dev/staging/prod targets
Unity Catalog	Models and data registered to UC for governance
MLflow Tracking	Experiment tracking and model versioning

📚 Resources

📜 License

Third-Party Licenses

Package	License	Copyright
mcp	MIT License	Copyright (c) 2024 Anthropic
requests	Apache License 2.0	Copyright 2019 Kenneth Reitz
python-dotenv	BSD 3-Clause License	Copyright (c) 2014, Saurabh Kumar