Databricks MCP Server App

Host the AI Dev Kit MCP server as a Databricks App — letting you experience 80+ Databricks tools from the AI Playground, no local setup required.

What This Is

A 3-file wrapper that takes the open-source databricks-mcp-server from the Databricks Solutions team (stdio transport) and deploys it as a Databricks App with Streamable HTTP transport. The Playground auto-discovers all tools.

app.py            # 4 lines — import server, expose as HTTP
app.yaml          # Databricks App config
requirements.txt  # Pull ai-dev-kit from GitHub
databricks.yml    # Databricks Asset Bundle config

Setup

Prerequisites

• Databricks CLI v0.229.0+ (databricks --version)
• A Databricks workspace with Apps enabled
• Authenticated CLI profile (databricks auth login --host <url>)

Deploy

This project uses Databricks Asset Bundles for deployment.

# Authenticate
databricks auth login --host https://your-workspace.cloud.databricks.com

# Validate the bundle
databricks bundle validate

# Deploy the app resource and sync source code
databricks bundle deploy

# Start the app (installs packages and launches the server)
databricks bundle run mcp_ai_dev_kit

# If using a named CLI profile, add --profile to each command:
databricks bundle deploy --profile <profile-name>
databricks bundle run mcp_ai_dev_kit --profile <profile-name>

Important: The app name must start with mcp- for the Playground to discover it as a custom MCP server. The default name mcp-ai-dev-kit already handles this.

Connect to AI Playground

1. Open your workspace → AI Playground
2. Select a model with the Tools enabled label
3. Click Tools → Add tool → MCP Servers
4. Add your app's MCP endpoint: https://<app-url>/mcp
5. The Playground auto-discovers all 80+ tools

Demo Script: Usage Dashboard in 3 Prompts

Once connected in the Playground:

1. "Query system.billing.usage and show me total DBUs by sku_name for the last 30 days" → Uses SQL tools

2. "Create a view called main.default.monthly_usage_summary that aggregates DBUs from system.billing.usage by month and sku_name" → Uses SQL tools

3. "Build a clean AI/BI dashboard that shows weekly and monthly usage trends from that view — a line chart for weekly DBUs over time and a bar chart for monthly DBUs by SKU" → Uses Dashboard tools

Switch to the workspace UI — a published Lakeview dashboard, built from conversation.

Architecture

AI Playground ──Streamable HTTP──▶ Databricks App (this repo)
                                        │
                                        ▼
                                  ai-dev-kit MCP Server
                                  (80+ tools via FastMCP)
                                        │
                                        ▼
                              Databricks APIs (SDK)
                              ├── SQL Warehouses
                              ├── Unity Catalog
                              ├── Jobs / Pipelines
                              ├── Vector Search
                              ├── Model Serving
                              ├── Agent Bricks
                              ├── AI/BI Dashboards
                              ├── Genie
                              └── ...