aymenfurter/bricks-use

Table of Contents

• Features
• Quick Start
• Setup
• CLI Usage
• MCP Tools
• VS Code Integration
• License

Features

Quick Start

# 1. Clone and setup
git clone https://github.com/aymenfurter/bricks-use.git
cd bricks-use
python -m venv .venv && source .venv/bin/activate

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure environment (see setup section)
cp .env.example .env  # Edit with your credentials

# 4. Run the server or use CLI
python databricks_server.py  # For MCP server
# OR
./bricks query "SELECT * FROM my_table LIMIT 10"  # For CLI

Setup

Prerequisites

Environment Variables

Set the following environment variables or create a .env file:

# Databricks Configuration
DATABRICKS_SERVER_HOSTNAME=your-workspace.cloud.databricks.com
DATABRICKS_HTTP_PATH=/sql/1.0/warehouses/your-warehouse-id
DATABRICKS_ACCESS_TOKEN=your-personal-access-token

# Optional Settings
DATABRICKS_CATALOG=main                    # Defaults to 'main'
DATABRICKS_SCHEMA=default                  # Defaults to 'default'
DATABRICKS_TEMP_DIR=/tmp/databricks_mcp    # Temp directory

Installation

1. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   

2. Install dependencies:

   pip install -r requirements.txt
   

CLI Usage

Use the ./bricks command-line tool for direct interaction:

# Execute SQL queries
./bricks query "SELECT * FROM my_table LIMIT 10"
./bricks query "SELECT COUNT(*) FROM users WHERE active = true" --limit 50

# Get table information
./bricks info my_table
./bricks info users --catalog production --schema analytics

# Compare tables
./bricks compare table1 table2
./bricks compare old_users new_users --quick
./bricks compare sales_2023 sales_2024 --catalog1 prod --schema1 sales

# Output options
./bricks query "SELECT * FROM table" --format json
./bricks info my_table --format table

MCP Tools

execute_query

Execute a SQL query on Databricks.

Parameters:

• query (str): SQL query to execute
• limit (int, optional): Maximum rows to return (default: 1000)

get_table_info

Get information about a Databricks table.

Parameters:

• table_name (str): Name of the table
• catalog (str, optional): Catalog name
• schema (str, optional): Schema name

compare_tables

Compare data between two tables by downloading full data and running diff.

Parameters:

• table1 (str): First table name
• table2 (str): Second table name
• catalog1 (str, optional): Catalog for table1
• schema1 (str, optional): Schema for table1
• catalog2 (str, optional): Catalog for table2
• schema2 (str, optional): Schema for table2
• diff_lines (int, optional): Number of diff context lines (default: 10)

quick_compare_tables

Quick metadata-only comparison without downloading data.

Parameters:

• table1 (str): First table name
• table2 (str): Second table name
• catalog1 (str, optional): Catalog for table1
• schema1 (str, optional): Schema for table1
• catalog2 (str, optional): Catalog for table2
• schema2 (str, optional): Schema for table2

VS Code MCP Integration

Add this configuration to your VS Code settings (mcp.json):

{
    "inputs": [
        {
            "type": "promptString",
            "id": "databricks_server_hostname",
            "description": "Databricks Server Hostname"
        },
        {
            "type": "promptString",
            "id": "databricks_http_path",
            "description": "Databricks HTTP Path"
        },
        {
            "type": "promptString",
            "id": "databricks_access_token",
            "description": "Databricks Access Token",
            "password": true
        },
        {
            "type": "promptString",
            "id": "databricks_catalog",
            "description": "Databricks Catalog (default: main)"
        },
        {
            "type": "promptString",
            "id": "databricks_schema",
            "description": "Databricks Schema (default: default)"
        }
    ],
    "servers": {
        "databricks": {
            "command": "python",
            "args": [
                "${workspaceFolder}/databricks_server.py"
            ],
            "env": {
                "PYTHONUNBUFFERED": "1",
                "DATABRICKS_SERVER_HOSTNAME": "${input:databricks_server_hostname}",
                "DATABRICKS_HTTP_PATH": "${input:databricks_http_path}",
                "DATABRICKS_ACCESS_TOKEN": "${input:databricks_access_token}",
                "DATABRICKS_CATALOG": "${input:databricks_catalog}",
                "DATABRICKS_SCHEMA": "${input:databricks_schema}"
            },
            "workingDirectory": "${workspaceFolder}"
        }
    }
}