Databricks MCP Genie

A Model Context Protocol (MCP) server with enhanced Genie AI integration that provides seamless natural language interaction between AI assistants (like Claude Desktop, Cursor) and Databricks workspaces.

What This Does

Enables AI assistants to directly interact with your Databricks workspace:

• Execute SQL queries and manage warehouses
• Control clusters (create, start, stop, monitor)
• Run jobs and notebooks
• Ask natural language questions with Genie AI
• Manage Unity Catalog (catalogs, schemas, tables)
• Work with DBFS, repos, and libraries

Quick Start

For Cursor Users

Team Installation (Recommended): See Cursor Setup Guide for one-click installation instructions.

Quick install:

pip install databricks-mcp-genie

Then configure in Cursor settings - full details in the Cursor Setup Guide.

Prerequisites

• Python 3.10 or higher
• Databricks workspace with personal access token
• Cursor IDE, Claude Desktop, or any MCP-compatible client

Installation

# Install from PyPI (recommended)
pip install databricks-mcp-genie

# Or install from source
git clone https://github.com/sidart10/databricks-mcp-genie.git
cd databricks-mcp-genie
pip install -e ".[dev]"

Configuration

1. Get your Databricks credentials:

   • Workspace URL: https://your-workspace.cloud.databricks.com
   • Personal Access Token: Generate from User Settings > Developer > Access Tokens

2. Configure MCP client (Claude Desktop example):

Edit ~/.config/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "databricks": {
      "command": "/path/to/databrics-mcp-server/.venv/bin/python",
      "args": ["-m", "databricks_mcp.main"],
      "cwd": "/path/to/databrics-mcp-server",
      "env": {
        "DATABRICKS_HOST": "https://your-workspace.cloud.databricks.com",
        "DATABRICKS_TOKEN": "your-personal-access-token-here"
      }
    }
  }
}

3. Restart Claude Desktop

Verify Installation

# Test server starts correctly
.venv/bin/python -m databricks_mcp.main

# Run test suite
.venv/bin/pytest tests/ -v

# Quick server test script
./test_server.sh

Available Features

43 MCP Tools Across 9 API Modules

Genie AI (5 tools) - Natural language data analysis

• list_genie_spaces - List available Genie AI spaces
• start_genie_conversation - Ask questions in natural language
• send_genie_followup - Continue conversations with context
• get_genie_message_status - Check message processing status
• get_genie_query_results - Retrieve SQL results from Genie

Clusters API (6 tools)

• list_clusters, create_cluster, get_cluster
• start_cluster, terminate_cluster

SQL API (1 tool)

• execute_sql - Run SQL queries with warehouse

Jobs API (9 tools)

• list_jobs, create_job, delete_job, run_job
• list_job_runs, get_run_status, cancel_run
• run_notebook, sync_repo_and_run_notebook

Notebooks API (5 tools)

• list_notebooks, export_notebook, import_notebook
• delete_workspace_object, get_workspace_file_content, get_workspace_file_info

DBFS API (3 tools)

• list_files, dbfs_put, dbfs_delete

Unity Catalog API (7 tools)

• list_catalogs, create_catalog
• list_schemas, create_schema
• list_tables, create_table, get_table_lineage

Repos API (4 tools)

• list_repos, create_repo, update_repo, pull_repo

Libraries API (3 tools)

• install_library, uninstall_library, list_cluster_libraries

Usage Examples

Using with Claude Desktop

Once configured, you can ask Claude to interact with Databricks:

"List all my running clusters"
"Execute this SQL query: SELECT * FROM my_catalog.my_schema.my_table LIMIT 10"
"Ask Genie: What were the top products by revenue last month?"
"Create a new job to run my ETL notebook daily"

Programmatic Usage

from databricks_mcp.server import DatabricksMCPServer

# Initialize server
server = DatabricksMCPServer()

# Use via MCP protocol
server.run()

Direct API Usage

from databricks_mcp.api import clusters, genie, sql

# List clusters
clusters_list = await clusters.list_clusters()

# Ask Genie a question
response = await genie.start_conversation(
    space_id="01efc298aabd1ae9bac6128988a6eaaa",
    question="Show me revenue trends by product category"
)

# Execute SQL
results = await sql.execute_sql(
    statement="SELECT * FROM sales.orders LIMIT 100",
    warehouse_id="your-warehouse-id"
)

Project Structure

databrics-mcp-server/
├── databricks_mcp/           # Main Python package
│   ├── api/                  # API modules (clusters, sql, genie, etc.)
│   ├── core/                 # Core utilities and config
│   ├── server/               # MCP server implementation
│   └── cli/                  # CLI commands
├── tests/                    # Test suite
├── examples/                 # Usage examples
├── scripts/                  # Utility scripts
├── docs/                     # Documentation
├── pyproject.toml           # Package configuration
├── .mcp.json                # MCP client configuration
└── test_server.sh           # Quick server test

Troubleshooting

Server Won't Start

Check logs: databricks_mcp.log

Common issues:

• Invalid credentials in .mcp.json
• Incorrect Python path in MCP config
• Missing dependencies (run pip install -e ".[dev]")

Import Errors

# Verify all imports work
.venv/bin/python -c "from databricks_mcp.server import DatabricksMCPServer"
.venv/bin/python -c "from databricks_mcp.api import clusters, sql, genie"

Connection Issues

Verify credentials:

export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-token"
.venv/bin/python -c "
from databricks_mcp.api import clusters
import asyncio
print(asyncio.run(clusters.list_clusters()))
"

See TROUBLESHOOTING.md for detailed solutions.

Development

Running Tests

# All tests
.venv/bin/pytest tests/ -v

# Specific test file
.venv/bin/pytest tests/test_clusters.py -v

# With coverage
.venv/bin/pytest tests/ --cov=databricks_mcp

Code Quality

# Format code
.venv/bin/black databricks_mcp/

# Lint
.venv/bin/pylint databricks_mcp/

Adding New Tools

1. Add API function in databricks_mcp/api/
2. Register tool in databricks_mcp/server/databricks_mcp_server.py:

@self.tool(
    name="your_tool_name",
    description="What your tool does with parameters: param1 (required), param2 (optional)"
)
async def your_tool(params: Dict[str, Any]) -> List[TextContent]:
    try:
        actual_params = _unwrap_params(params)
        result = await your_api_module.your_function(actual_params)
        return [{"type": "text", "text": json.dumps(result)}]
    except Exception as e:
        logger.error(f"Error: {str(e)}")
        return [{"type": "text", "text": json.dumps({"error": str(e)})}]

Documentation

Setup & Installation

• Cursor Setup Guide - One-click installation for Cursor (recommended for teams)
• QUICK_START.md - Getting started guide
• Deployment Summary - Package distribution overview

Development & Publishing

• PUBLISHING.md - How to publish to PyPI
• TROUBLESHOOTING.md - Common issues and solutions
• ENHANCEMENTS.md - Feature enhancements and roadmap

Requirements

• Python >=3.10
• mcp[cli] >=1.2.0
• httpx
• databricks-sdk
• pytest (dev)
• black (dev)
• pylint (dev)

License

MIT License - See LICENSE file for details

Acknowledgments

Package: databricks-mcp-genie Maintainer: Sid Original Author: Olivier Debeuf De Rijcker (databricks-mcp) Repository: https://github.com/sidart10/databricks-mcp-genie

Special thanks to:

• Olivier Debeuf De Rijcker for the original databricks-mcp implementation
• Anthropic for Claude and the MCP protocol
• Databricks for their comprehensive SDK and Genie AI
• The open source community

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Built with Claude Code - AI-assisted development tool by Anthropic