# Databricks MCP Server - Project Summary
## Overview
We've successfully created a Databricks MCP (Model Context Protocol) server that provides tools for interacting with Databricks APIs. The server follows the MCP standard, which allows AI models to interact with external tools and services in a standardized way.
## Key Accomplishments
1. **Server Implementation**:
- Created a `DatabricksMCPServer` class that inherits from `FastMCP`
- Implemented the MCP protocol for communication with clients
- Set up proper error handling and logging
2. **Tool Registration**:
- Registered tools for managing Databricks resources
- Implemented proper parameter validation and error handling
- Added detailed descriptions for each tool
3. **API Integration**:
- Implemented functions for interacting with Databricks APIs
- Set up proper authentication using Databricks tokens
- Added error handling for API requests
4. **Testing**:
- Created a direct test script to verify server functionality
- Successfully tested the `list_clusters` tool
- Verified that the server can connect to Databricks and retrieve data
5. **Documentation**:
- Created a README file with installation and usage instructions
- Documented available tools and their parameters
- Added a requirements.txt file with necessary dependencies
## Next Steps
1. **Additional Tools**:
- Implement more tools for managing Databricks resources
- Add support for Unity Catalog management
- Add support for Delta Live Tables pipelines
2. **Enhanced Testing**:
- Create more comprehensive test scripts
- Add unit tests for individual components
- Set up continuous integration
3. **Deployment**:
- Create Docker container for easy deployment
- Add support for running as a service
- Implement authentication for the MCP server
4. **Client Integration**:
- Create example clients for different AI models
- Add support for popular AI platforms
- Create documentation for client integration