Turn Your LLM Into a Databricks Power User

Stop jumping between Claude conversations and your Databricks workspace. This MCP server lets your AI assistant query your data warehouse, check job statuses, and run SQL directly from natural language requests.

The Problem: Context Switching Kills Flow

You're analyzing data with an LLM when you need to check a table schema in Databricks. Or verify if your ETL job finished. Or run a quick query to validate assumptions. Each time, you tab over to Databricks, lose your train of thought, and manually bridge the gap between AI insights and your actual data.

That friction adds up. Fast.

What This Changes

This MCP server connects your AI assistant directly to your Databricks workspace. Now you can:

• Query live data: "Show me the top 10 customers by revenue this quarter" gets executed against your actual warehouse
• Monitor jobs: "Is the daily ETL job still running?" returns real status without opening another tab
• Explore schemas: "What columns are in the customer_events table?" pulls live metadata
• Debug pipelines: "Check the last run status for job 1234" gives you instant feedback

Your LLM conversation becomes your data workspace.

Real Workflow Impact

Before: You're building a customer segmentation model with Claude. You need to check if the customer_features table has the recency column. Tab to Databricks, log in, navigate to catalog, find table, check schema, remember what you were doing, tab back, continue conversation.

After: "Does customer_features have a recency column?" Claude queries your warehouse directly and shows you the schema. Zero context switching. The conversation flows naturally from data exploration to analysis to insights.

Before: Your ML training job should be done by now. Open Databricks Jobs, find job ID, check status, note completion time, figure out next steps.

After: "How did job 5678 go?" Your AI tells you it completed successfully 10 minutes ago and can help you analyze the results immediately.

Integration Reality

This isn't another tool to learn. It's Python-based, uses standard Databricks credentials, and connects through environment variables you're already managing:

DATABRICKS_HOST=your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=your-personal-access-token  
DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/your-warehouse-id

Setup takes 5 minutes. The MCP inspector lets you test connections before deploying. No API learning curves or authentication headaches.

When This Shines

• Data exploration sessions: Instead of writing SQL in Databricks then explaining results to your AI, have the AI write and execute the SQL based on your questions
• Job monitoring: Check pipeline health during long conversations without losing focus
• Schema discovery: Understand data structures as part of natural problem-solving flow
• Quick validation: Test hypotheses against real data without breaking conversational context
• Cross-system debugging: Connect data pipeline issues with analysis workflows in one thread

The Productivity Math

Consider how often you switch between AI conversations and Databricks. Each switch costs 2-3 minutes of mental overhead. If you do this 10 times a day, you're losing 30 minutes to context switching alone.

This MCP server eliminates that friction entirely. Your data and your AI assistant become one integrated workspace where insights flow naturally from questions to queries to answers.

Perfect for data scientists, analytics engineers, and anyone who spends their day bridging AI capabilities with actual data infrastructure. The 33 stars and 16 forks suggest developers are already finding real value here.

Time to make your AI conversations as smart as your data.