Databricks MCP Server

# Databricks MCP Server Documentation Welcome to the comprehensive documentation for the Databricks MCP (Model Context Protocol) Server. This server provides AI assistants with programmatic access to Databricks services through a unified interface. ## 📚 Documentation Overview This documentation is organized into logical sections that cover all aspects of the Databricks MCP server: ### 🚀 [Getting Started](getting-started.md) - Quick start guide - Installation and setup - Authentication configuration - Basic usage examples ### 🔧 [Core Tools & APIs](core-tools.md) - **Databricks SDK Integration**: Direct SDK usage patterns and examples - **Unity Catalog Tools**: Complete data catalog management capabilities - **SQL Operations**: Warehouse management and query execution - **Compute Management**: Clusters, jobs, and workflows - **File Management**: DBFS and workspace file operations ### 🏗️ [Architecture & Design](architecture.md) - System architecture overview - Tool implementation patterns - Error handling and validation - Performance considerations ### 📖 [API Reference](api-reference.md) - Complete tool reference - Parameter specifications - Return value documentation - Usage examples for each tool ### 🧪 [Testing & Development](testing.md) - Testing strategies and tools - Development setup - Debugging and troubleshooting - Contributing guidelines ## 🎯 What This Server Provides The Databricks MCP Server exposes over **100+ tools** covering: - **Data Management**: Unity Catalog, tables, schemas, volumes - **Compute Resources**: Clusters, SQL warehouses, instance pools - **Workflows**: Jobs, pipelines, Delta Live Tables - **Machine Learning**: Model serving, experiments, feature store - **File Operations**: DBFS, workspace files, external storage - **Governance**: Permissions, audit logs, data quality monitoring - **Development**: Git integration, notebooks, repositories ## 🚀 Quick Start ### 1. Installation ```bash # Clone the repository git clone https://github.com/your-org/awesome-databricks-mcp.git cd awesome-databricks-mcp # Install dependencies pip install -r requirements.txt ``` ### 2. Configuration ```bash # Set environment variables export DATABRICKS_HOST="https://your-workspace.cloud.databricks.com" export DATABRICKS_TOKEN="your-personal-access-token" ``` ### 3. Start the Server ```bash # Run locally ./run_app_local.sh # Or deploy to production ./deploy.sh ``` ## 🔑 Key Features - **Comprehensive Coverage**: Access to all major Databricks services - **Unified Interface**: Consistent tool patterns across all services - **Error Handling**: Robust error handling with detailed feedback - **Performance Optimized**: Pagination, caching, and async operations - **Security Focused**: Proper authentication and permission handling - **Developer Friendly**: Clear documentation and examples ## 📊 Tool Categories | Category | Tools | Status | |----------|-------|---------| | **Unity Catalog** | 15+ tools | ✅ Complete | | **SQL Operations** | 10+ tools | ✅ Complete | | **Compute Management** | 20+ tools | 🔄 In Progress | | **Jobs & Workflows** | 15+ tools | ✅ Complete | | **File Management** | 12+ tools | ✅ Complete | | **ML & AI** | 25+ tools | 🔄 In Progress | | **Governance** | 10+ tools | ✅ Complete | ## 🛠️ Technology Stack - **Backend**: FastAPI + Python - **Databricks Integration**: Official Python SDK - **Authentication**: Personal Access Tokens, Service Principals - **Documentation**: Markdown + Auto-generated API docs - **Testing**: pytest + Integration tests ## 📖 Detailed Documentation ### [Databricks SDK Guide](databricks_apis/databricks_sdk.md) Comprehensive guide to using the Databricks SDK directly, including: - Authentication and setup - Core API patterns - FastAPI integration examples - Error handling best practices ### [Unity Catalog Tools](unity_catalog_tools.md) Complete reference for Unity Catalog operations: - Catalog, schema, and table management - Volume and function operations - Data discovery and search - Governance and permissions ### [Model Serving APIs](databricks_apis/model_serving.md) Detailed guide to ML model serving: - Endpoint management - Real-time and batch inference - Chat model integration - Error handling and monitoring ### [SDK Tools Implementation](databricks_sdk_tools.md) Implementation plan and reference for all SDK tools: - Tool categorization and priorities - Implementation status tracking - Testing strategies - Future enhancements ## 🔍 Search & Navigation Use the table of contents above to navigate to specific sections, or search for specific topics: - **For SDK usage**: See [Databricks SDK Guide](databricks_apis/databricks_sdk.md) - **For Unity Catalog**: See [Unity Catalog Tools](unity_catalog_tools.md) - **For ML models**: See [Model Serving APIs](databricks_apis/model_serving.md) - **For tool development**: See [SDK Tools Implementation](databricks_sdk_tools.md) ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for: - Code style guidelines - Testing requirements - Pull request process - Development setup ## 📞 Support - **Documentation Issues**: Open an issue in this repository - **Code Problems**: Check the [Troubleshooting Guide](troubleshooting.md) - **Feature Requests**: Use the issue tracker with the "enhancement" label ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE.md) file for details. --- **Last Updated**: December 2024 **Version**: 1.0.0 **Databricks SDK Version**: 0.59.0