Databricks

🚀 Databricks Model Completion Protocol (MCP) Server

The Databricks MCP Server is a tool for interacting with the Databricks platform. It enables developers to perform data processing and analysis through the Model Completion Protocol (MCP) interface.

🚀 Quick Start

The Databricks MCP Server allows developers to conduct data processing and analysis via the MCP interface. Here is a quick guide to get you started.

✨ Features

• Multithreading Support: The server supports multithreaded processing, capable of handling multiple client requests simultaneously.
• High-Performance Computing: Optimized for computational performance, suitable for large-scale data analysis tasks.
• Scalable Architecture: The modular design allows for easy feature expansion and third-party service integration.
• Security and Authentication: Built-in authentication mechanisms ensure the security of data and communication.

📦 Installation

System Requirements

• Operating System: Supports Linux, Windows, and macOS.
• Memory: A minimum of 4GB RAM is recommended.
• Disk Space: At least 50GB of free space is required.

Installation Steps

1. Download the Installation Package:

   # Download the latest version of the MCP Server
   wget https://example.com/mcp-server-latest.tar.gz
   

2. Extract and Install:

   tar -xzvf mcp-server-latest.tar.gz
   cd mcp-server/
   ./install.sh
   

3. Configure Environment Variables (Optional):

   export PATH=/path/to/mcp-server/bin:$PATH
   

4. Start the Service:

   mcp-server start
   

💻 Usage Examples

Basic Usage

from databricks_mcp_server import MCPClient

client = MCPClient(host='localhost', port=5000)
response = client.request('list_clusters')
print(response)

Advanced Usage

import requests

url = 'http://localhost:5000/api/v1/clusters'
response = requests.get(url)
print(response.json())

📚 Documentation

Coding Standards

• Follow the PEP 8 code style guide, with a maximum of 100 characters per line.
• Use 4 spaces for indentation (no tabs).
• Use double quotes for strings.
• All classes, methods, and functions must include Google-style docstrings.
• All code, except test code, requires type hints.

Review Tools

The project uses the following tools for code inspection:

# Run all review tools
linter run pylint src/ tests/
linter run flake8 src/ tests/
linter run mypy src/

Testing Guide

Use pytest for testing. To run tests:

# Run all tests
.\scripts\run_tests.ps1

# Generate a coverage report
.\scripts\run_tests.ps1 -Coverage

# Run specific tests with verbose output
.\scripts\run_tests.ps1 -Verbose -Coverage tests/test_clusters.py

Documentation Generation

• Use Sphinx to generate API documentation, located in the docs/api directory.
• All code includes Google-style docstrings.
• Check the usage examples in the examples/ directory.

Example Execution

Check the usage examples in the examples/ directory. To run example scripts:

# Run example scripts
uv run examples/direct_usage.py
uv run examples/mcp_client_usage.py

Contribution Guidelines

Contributions are welcome! Please follow these steps to submit a pull request:

1. Ensure your code adheres to the project's coding standards.
2. Add tests for any new features.
3. Update relevant documentation.
4. Ensure all tests pass before submission.

📄 License

This project is released under the MIT License. Please refer to the LICENSE file for details.