Databricks MCP Server: Integrating SQL Queries into AI Workflows

Overview: What is Databricks MCP Server?

Databricks MCP Server is a specialized Model Context Protocol (MCP) server designed to execute SQL queries against Databricks using the Statement Execution API. It serves as a bridge between AI applications and complex data environments, allowing seamless execution of database operations through standardized APIs provided by the Model Context Protocol library. By leveraging this server, AI applications can perform intricate tasks such as listing schemas, querying specific tables, and describing their structures.

Key Features & MCP Capabilities

Databricks MCP Server offers robust capabilities that enhance AI application performance:

SQL Execution: Execute SQL queries against Databricks to retrieve data.
Schema Navigation: List available schemas within catalogs, making it easier to understand and navigate data sources.
Table Management: List tables in a specified schema to manage query operations effectively.
Schema Description: Describe the structure of specific tables for detailed analysis.

These features make Databricks MCP Server an essential component for AI applications that require direct interaction with databases through MCP.

MCP Architecture & Protocol Implementation

The server implements the Model Context Protocol (MCP) by executing SQL queries via the Statement Execution API. It leverages Python and relevant libraries such as httpx, python-dotenv, and mcp to ensure seamless integration with Databricks. The architecture is designed to handle long-running queries using a polling mechanism, ensuring that queries are completed within predefined intervals.

Getting Started with Installation

System Requirements

To install and run the Databricks MCP Server effectively, you must meet the following system requirements:

Python Version: Python 3.10 or later.
UV Environment (Optional): Ensure it is installed if using uv for installation commands.

Installation Steps

Follow these steps to set up the server on your local machine:

Install Dependencies:
```
pip install -r requirements.txt
```

Set Up Environment Variables:

Option 1: Use a .env file.

Create a .env file with Databricks credentials:

DATABRICKS_HOST=your-databricks-instance.cloud.databricks.com
DATABRICKS_TOKEN=your-databricks-access-token
DATABRICKS_SQL_WAREHOUSE_ID=your-sql-warehouse-id

Option 2: Set environment variables directly.

export DATABRICKS_HOST="your-databricks-instance.cloud.databricks.com"
export DATABRICKS_TOKEN="your-databricks-access-token"
export DATABRICKS_SQL_WAREHOUSE_ID="your-sql-warehouse-id"

Running the Server:

To run the server in standalone mode:
```
python main.py
```

For running with Cursor, follow these additional steps:

Create a .cursor directory if it doesn't exist.
Add configuration to mcp.json file.

Integration with MCP Clients

Databricks MCP Server supports integration with various MCP clients such as Claude Desktop, Continue, and Cursor through the configured settings in their respective client environments.

Key Use Cases in AI Workflows

Real-Time Data Access

In this scenario, an AI application needs real-time data access to a Databricks-managed SQL warehouse. The Databricks MCP Server can execute complex queries on large datasets and provide instant insights, allowing the AI application to make dynamic decisions based on fresh information.

Technical Implementation: The server's execute_sql_query tool is used to run SQL commands that fetch real-time data for analysis or prediction purposes. This ensures that the AI application always operates with the most current dataset available in Databricks.

Data-Driven Decision Making

This use case involves using historical and real-time data from Databricks for informed decision-making processes within an AI-driven business environment.

Technical Implementation: The server's list_schemas, list_tables, and describe_table tools are employed to navigate the schema structure of complex databases. These methods help in identifying relevant tables, understanding their schemas, and generating queries that extract specific data for analysis or predictive modeling tasks.

Performance & Compatibility Matrix

MCP Client Compatibility

The Databricks MCP Server is designed with compatibility in mind, ensuring seamless integration across various AI applications:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

This matrix highlights that while support for tools and resources is universally present, not all clients provide full compatibility in terms of AI prompt generation.

Configuration Code Sample

Example configuration for the MCP client:

{
  "mcpServers": {
    "databricksMCP": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-databricks"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

This configuration ensures that Databricks MCP Server is correctly initialized and ready for use with any compatible MC client.

Advanced Configuration & Security

Handling Long-Running Queries

Databricks MCP Server is configured to handle long-running queries efficiently by polling the Databricks API periodically. By default, this interval is set to 60 seconds (10 retries), but configurations can be adjusted in dbapi.py for different needs.

Permissions & Security Best Practices

Before using this server, ensure that you have appropriate permissions:

SQL Warehouse: The associated user should have the necessary permissions.
Personal Access Tokens: Use dedicated tokens and restrict their capabilities to minimize security risks.

APIs such as GET /api/2.0/sql/permissions/warehouses/{warehouse_id} are used for checking and updating permissions.

Frequently Asked Questions (FAQ)

Q1: How do I ensure that my personal access token is secure?

A1: Generate a dedicated token with minimal privileges to limit potential risks. Regularly rotate your tokens to maintain security practices.

Q2: What is the default timeout setting for long-running queries?

A2: The default timeout setting in Databricks MCP Server is 60 seconds (10 retries with 10-second intervals). This can be customized by modifying dbapi.py if needed.

Q3: Can I run this server on a remote machine or cloud service?

A3: Yes, the server runs independently and can be deployed in a remote location. Ensure that Databricks API endpoints are accessible over the network.

Q4: How often does the server poll for long-running query results?

A4: The polling interval is set at 10 seconds by default, with 60 retries until the query completes or times out.

Q5: Are there any specific tools that do not work with Databricks MCP Server?

A5: The Cursor client does not fully support prompts. Other clients like Claude Desktop and Continue are fully compatible for resources and tools functionality.

Development & Contribution Guidelines

Contributions to the Databricks MCP Server are welcome! Developers can enhance the server’s capabilities or fix bugs by following these guidelines:

Fork the Repository: Start by forking the repository on GitHub.
Set Up Local Environment: Follow the installation steps provided in the README, ensuring a local development environment is configured properly.
Run Tests: Make sure tests pass before making any changes.
Commit Changes: Commit your changes with clear descriptions and run make build to re-package the server.
Push & Pull Requests: Push your commits to a branch on GitHub and create a pull request.

MCP Ecosystem & Resources

Mermaid Diagrams

MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Data Architecture

graph TD
    A[AI Application] -->|API Calls| B[MCP Server]
    B --> C[Data Source]
    D[Database/Tool]-->E[Query Responses/Batch Data]
    E --> F[MCCache/Persistent Storage]
    G[Fetched Data/MC Context]--->H[Databricks MCP Server]

Conclusion

By integrating the Databricks MCP Server with AI applications, developers can leverage powerful data management and analysis tools while maintaining compatibility and security. This server enhances the versatility of AI workflows by providing a robust, standardized interface for interacting with Databricks SQL warehouses.

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Databricks MCP Server