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4 months ago
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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!
| 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 |
# 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
python databricks_server.py
| Python | 3.11 or higher |
| Databricks | Workspace access |
| Token | Personal access token |
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
-
Create and activate a virtual environment:
python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate -
Install dependencies:
pip install -r requirements.txt -
Run the server:
python databricks_server.py
| 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 a SQL query on Databricks.
Parameters:
- query (str): SQL query to execute
- limit (int, optional): Maximum rows to return (default: 1000)
Example:
Execute this query: SELECT * FROM my_table WHERE status = 'active'
Get information about a Databricks table.
Parameters:
- table_name (str): Name of the table
- catalog (str, optional): Catalog name
- schema (str, optional): Schema name
Example:
Get info for table 'users' in catalog 'production' and schema 'analytics'
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)
Example:
Compare tables 'users_old' and 'users_new' and show differences
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
Example:
Quick compare 'table_a' and 'table_b' schemas and row counts
Add this configuration to your VS Code settings (mcp.json):
Click to expand VS Code configuration{
"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}"
}
}
}
Execute this SQL query: SELECT customer_id, order_date, total_amount
FROM orders
WHERE order_date >= '2024-01-01'
LIMIT 100
Compare tables 'sales_2023' and 'sales_2024' and show me the differences
Quick compare the schemas of 'old_users' and 'new_users' tables
This project is licensed under the MIT License.
Made with ❤️ for the Databricks community
