sash-mcp-server

sashankkotta-db-org/sash-mcp-server

3.1

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The Databricks MCP Server Template enables seamless integration between AI assistants like Claude and your Databricks workspace through a secure, authenticated interface.

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Resources
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Prompts
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🤖 Databricks MCP Server Template

Host Model Context Protocol (MCP) prompts and tools on Databricks Apps, enabling AI assistants like Claude to interact with your Databricks workspace through a secure, authenticated interface.

What is this?

This template lets you create an MCP server that runs on Databricks Apps. You can:

  • 📝 Add prompts as simple markdown files in the prompts/ folder
  • 🛠️ Create tools as Python functions that leverage Databricks SDK
  • 🔐 Authenticate securely with OAuth through Databricks Apps
  • 🚀 Deploy instantly to make your MCP server accessible to Claude

Think of it as a bridge between Claude and your Databricks workspace - you define what Claude can see and do, and this server handles the rest.

How it Works

Architecture Overview

┌─────────────┐       MCP Protocol      ┌──────────────────┐        OAuth         ┌─────────────────┐
│   Claude    │ ◄─────────────────────► │  dba-mcp-proxy   │ ◄──────────────────► │ Databricks App  │
│    CLI      │     (stdio/JSON-RPC)    │ (local process)  │    (HTTPS/SSE)      │  (MCP Server)   │
└─────────────┘                         └──────────────────┘                      └─────────────────┘
                                                ▲                                           │
                                                │                                           ▼
                                                └────────── Databricks OAuth ──────► Workspace APIs

Components

  1. MCP Server (server/app.py): A FastAPI app with integrated MCP server that:

    • Dynamically loads prompts from prompts/*.md files
    • Exposes Python functions as MCP tools via @mcp_server.tool decorator
    • Handles both HTTP requests and MCP protocol over Server-Sent Events

  2. Prompts (prompts/): Simple markdown files where:

    • Filename = prompt name (e.g., check_system.md → check_system prompt)
    • First line with # = description
    • File content = what gets returned to Claude

  3. Local Proxy (dba_mcp_proxy/): Authenticates and proxies MCP requests:

    • Handles Databricks OAuth authentication automatically
    • Translates between Claude's stdio protocol and HTTP/SSE
    • Works with both local development and deployed apps

🎬 Demo

This 10-minute video shows you how to set up and use a Databricks MCP server with Claude: https://www.youtube.com/watch?v=oKE59zgb6e0

This video demonstrates creating your own MCP server with a custom jobs interface in Claude.

Quick Start

Create Your Own MCP Server

Step 1: Use this template

Use this template

Or use the GitHub CLI:

gh repo create my-mcp-server --template databricks-solutions/custom-mcp-databricks-app --private
Step 2: Clone and setup
# Clone your new repository
git clone https://github.com/YOUR-USERNAME/my-mcp-server.git
cd my-mcp-server

# Run the interactive setup
./setup.sh

This will:

  • Configure Databricks authentication
  • Set your MCP server name
  • Install all dependencies
  • Create your .env.local file
Step 3: Deploy with Claude

In Claude Code, run:

/setup-mcp

This will:

  • Deploy your MCP server to Databricks Apps
  • Configure the MCP integration
  • Show you available prompts and tools

Then restart Claude Code to use your new MCP server.

Add to Claude CLI

After deployment, add your MCP server to Claude:

# Set your Databricks configuration
export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_APP_URL="https://your-app.databricksapps.com"  # Get this from ./app_status.sh
export SERVER_NAME="your-server-name"  # This comes from config.yaml (set during ./setup.sh)

# Add your MCP server to Claude (user-scoped)
claude mcp add $SERVER_NAME --scope user -- \
  uvx --from git+ssh://git@github.com/YOUR-USERNAME/your-repo.git dba-mcp-proxy \
  --databricks-host $DATABRICKS_HOST \
  --databricks-app-url $DATABRICKS_APP_URL

Local Development

# Clone and setup
git clone <your-repo>
cd <your-repo>
./setup.sh

# Start dev server
./watch.sh

# Set your configuration for local testing
export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_APP_URL="http://localhost:8000"  # Local dev server

# Add to Claude for local testing
claude mcp add databricks-mcp-local --scope local -- \
  uvx --from git+ssh://git@github.com/YOUR-ORG/YOUR-REPO.git dba-mcp-proxy \
  --databricks-host $DATABRICKS_HOST \
  --databricks-app-url $DATABRICKS_APP_URL

Customization Guide

This template uses FastMCP, a framework that makes it easy to build MCP servers. FastMCP provides two main decorators for extending functionality:

  • @mcp_server.prompt - For registering prompts that return text
  • @mcp_server.tool - For registering tools that execute functions

Adding Prompts

The easiest way is to create a markdown file in the prompts/ directory:

# Get cluster information

List all available clusters in the workspace with their current status

The prompt will be automatically loaded with:

  • Name: filename without extension (e.g., get_clusters.md → get_clusters)
  • Description: first line after #
  • Content: entire file content

Alternatively, you can register prompts as functions in server/app.py:

@mcp_server.prompt(name="dynamic_status", description="Get dynamic system status")
async def get_dynamic_status():
    # This can include dynamic logic, API calls, etc.
    w = get_workspace_client()
    current_user = w.current_user.me()
    return f"Current user: {current_user.display_name}\nWorkspace: {DATABRICKS_HOST}"

We auto-load prompts/ for convenience, but function-based prompts are useful when you need dynamic content.

Adding Tools

Add a function in server/app.py using the @mcp_server.tool decorator:

@mcp_server.tool
def list_clusters(status: str = "RUNNING") -> dict:
    """List Databricks clusters by status."""
    w = get_workspace_client()
    clusters = []
    for cluster in w.clusters.list():
        if cluster.state.name == status:
            clusters.append({
                "id": cluster.cluster_id,
                "name": cluster.cluster_name,
                "state": cluster.state.name
            })
    return {"clusters": clusters}

Tools must:

  • Use the @mcp_server.tool decorator
  • Have a docstring (becomes the tool description)
  • Return JSON-serializable data (dict, list, str, etc.)
  • Accept only JSON-serializable parameters

Deployment

# Deploy to Databricks Apps
./deploy.sh

# Check status and get your app URL
./app_status.sh

Your MCP server will be available at https://your-app.databricksapps.com/mcp/

The app_status.sh script will show your deployed app URL, which you'll need for the DATABRICKS_APP_URL environment variable when adding the MCP server to Claude.

Authentication

  • Local Development: No authentication required
  • Production: OAuth is handled automatically by the proxy using your Databricks CLI credentials

Examples

Using with Claude

Once added, you can interact with your MCP server in Claude:

Human: What prompts are available?

Claude: I can see the following prompts from your Databricks MCP server:
- check_system: Get system information
- list_files: List files in the current directory
- ping_google: Check network connectivity

Sample Tool Usage

Human: Can you execute a SQL query to show databases?

Claude: I'll execute that SQL query for you using the execute_dbsql tool.

[Executes SQL and returns results]

Project Structure

├── server/                    # FastAPI backend with MCP server
│   ├── app.py                # Main application + MCP tools
│   └── routers/              # API endpoints
├── prompts/                  # MCP prompts (markdown files)
│   ├── check_system.md      
│   ├── list_files.md        
│   └── ping_google.md       
├── dba_mcp_proxy/           # MCP proxy for Claude CLI
│   └── mcp_client.py        # OAuth + proxy implementation
├── client/                  # React frontend (optional)
├── scripts/                 # Development tools
└── pyproject.toml          # Python package configuration

Advanced Usage

Environment Variables

Configure in .env.local:

DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
DATABRICKS_TOKEN=your-token  # For local development
DATABRICKS_SQL_WAREHOUSE_ID=your-warehouse-id  # For SQL tools

Creating Complex Tools

Tools can access the full Databricks SDK:

@mcp_server.tool
def create_job(name: str, notebook_path: str, cluster_id: str) -> dict:
    """Create a Databricks job."""
    w = get_workspace_client()
    job = w.jobs.create(
        name=name,
        tasks=[{
            "task_key": "main",
            "notebook_task": {"notebook_path": notebook_path},
            "existing_cluster_id": cluster_id
        }]
    )
    return {"job_id": job.job_id, "run_now_url": f"{DATABRICKS_HOST}/#job/{job.job_id}"}

Testing Your MCP Server

After adding the MCP server to Claude, verify it's working:

# List available prompts and tools
echo "What MCP prompts are available from databricks-mcp?" | claude

# Test a specific prompt
echo "Use the check_system prompt from databricks-mcp" | claude

Troubleshooting

  • Authentication errors: Run databricks auth login to refresh credentials
  • MCP not found: Ensure the app is deployed and accessible
  • Tool errors: Check logs at https://your-app.databricksapps.com/logz
  • MCP connection issues:
    • Check Claude logs: tail -f ~/Library/Logs/Claude/*.log
    • Verify the proxy works: uvx --from git+ssh://... dba-mcp-proxy --help
    • Test with echo pipe: echo "list your mcp commands" | claude
  • Cached version issues: If you get errors about missing arguments after an update: 
    # Clear uvx cache for this package
rm -rf ~/.cache/uv/git-v0/checkouts/*/
# Or clear entire uv cache
uv cache clean

Contributing

  1. Fork the repository
  2. Add your prompts and tools
  3. Test locally with ./watch.sh
  4. Submit a pull request

License

See