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aymenfurter

bricks-use

by aymenfurter
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Python
Remote

CI/CD Pipeline Python 3.11+ License: MIT Code style: black Type Checked: mypy Databricks

A powerful Model Context Protocol (MCP) server for executing Databricks SQL queries and comparing table data.

⚠️ This project is purely meant for demo purposes - use at your own risk!

Table of Contents

Features

Feature

Description

Execute SQL Queries

Run any SQL query on Databricks with configurable result limits

Table Information

Get detailed information about tables including schema and row counts

Table Comparison

Compare two tables by downloading their data and running CLI diff

Quick Comparison

Fast metadata-only comparison of tables

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

Tool

Purpose

Key Parameters

execute_query

Execute SQL queries

query, limit

get_table_info

Get table metadata

table_name, catalog, schema

compare_tables

Full data comparison

table1, table2, diff_lines

quick_compare_tables

Metadata comparison

table1, table2

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}"
        }
    }
}

License

This project is licensed under the MIT License.

Made with ❤️ for the Databricks community