How-To Guides
These guides cover specific tasks and workflows. Each guide assumes you’ve already installed the package (Getting Started).
Common tasks:
- Querying Cloud-Optimised Data
- Dataset Configuration
- Adding the dataset to pyproject.toml
- Configuration Validation
- Cluster Configuration & Distributed Processing
- Notebooks
- MCP Server
- MCP Server Installation
- Starting the Server
- Gemini CLI (Linux / Ubuntu)
- GitHub Copilot CLI (Linux)
- GitHub Copilot in VS Code (Linux)
- Claude Desktop Configuration (macOS / Windows)
- Environment Variables
- Available MCP Tools
- Available MCP Resources
- Example AI Prompts
- Notebook Builder Workflow
- Known Code Pitfalls Avoided by the Server
- Dataset–Notebook Mapping
- Testing
- Update All Metadata Script
I want to…
Query and analyze cloud-optimized data: Querying Cloud-Optimised Data
Create a cloud-optimized dataset from NetCDF/CSV files: Dataset Configuration
Process large datasets in parallel: Cluster Configuration & Distributed Processing
Write and share a Jupyter notebook for data analysis: Notebooks
Set up the MCP (Model Context Protocol) server: MCP Server
Update metadata in the AODN catalog: Update All Metadata Script
Each guide includes examples and troubleshooting tips for common issues.