Databricks MCP Server

Features

• Run SQL queries on Databricks SQL warehouses

• List all Databricks jobs

• Get status of specific Databricks jobs

• Get detailed information about Databricks jobs

Prerequisites

• Python 3.7+

• Databricks workspace with:

Setup

1. Clone this repository

2. Create and activate a virtual environment (recommended):

3. Install dependencies:

4. Create a .env file in the root directory with the following variables:

5. Test your connection (optional but recommended):

Obtaining Databricks Credentials

1. Host: Your Databricks instance URL (e.g., your-instance.cloud.databricks.com)

2. Token: Create a personal access token in Databricks:

3. HTTP Path: For your SQL warehouse:

Running the Server

Available MCP Tools

1. run_sql_query(sql: str) - Execute SQL queries on your Databricks SQL warehouse

2. list_jobs() - List all Databricks jobs in your workspace

3. get_job_status(job_id: int) - Get the status of a specific Databricks job by ID

4. get_job_details(job_id: int) - Get detailed information about a specific Databricks job

Example Usage with LLMs

• "Show me all tables in the database"

• "Run a query to count records in the customer table"

• "List all my Databricks jobs"

• "Check the status of job #123"

• "Show me details about job #456"

Troubleshooting

Connection Issues

• Ensure your Databricks host is correct and doesn't include https:// prefix

• Check that your SQL warehouse is running and accessible

• Verify your personal access token has the necessary permissions

• Run the included test script: python test_connection.py

Security Considerations

• Your Databricks personal access token provides direct access to your workspace

• Secure your .env file and never commit it to version control

• Consider using Databricks token with appropriate permission scopes only

• Run this server in a secure environment

Databricks MCP Server

Features

• Run SQL queries on Databricks SQL warehouses

• List all Databricks jobs

• Get status of specific Databricks jobs

• Get detailed information about Databricks jobs

Prerequisites

• Python 3.7+

• Databricks workspace with:

Setup

1. Clone this repository

2. Create and activate a virtual environment (recommended):

3. Install dependencies:

4. Create a .env file in the root directory with the following variables:

5. Test your connection (optional but recommended):

Obtaining Databricks Credentials

1. Host: Your Databricks instance URL (e.g., your-instance.cloud.databricks.com)

2. Token: Create a personal access token in Databricks:

3. HTTP Path: For your SQL warehouse:

Running the Server

Available MCP Tools

1. run_sql_query(sql: str) - Execute SQL queries on your Databricks SQL warehouse

2. list_jobs() - List all Databricks jobs in your workspace

3. get_job_status(job_id: int) - Get the status of a specific Databricks job by ID

4. get_job_details(job_id: int) - Get detailed information about a specific Databricks job

Example Usage with LLMs

• "Show me all tables in the database"

• "Run a query to count records in the customer table"

• "List all my Databricks jobs"

• "Check the status of job #123"

• "Show me details about job #456"

Troubleshooting

Connection Issues

• Ensure your Databricks host is correct and doesn't include https:// prefix

• Check that your SQL warehouse is running and accessible

• Verify your personal access token has the necessary permissions

• Run the included test script: python test_connection.py

Security Considerations

• Your Databricks personal access token provides direct access to your workspace

• Secure your .env file and never commit it to version control

• Consider using Databricks token with appropriate permission scopes only

• Run this server in a secure environment