Learn how to seamlessly integrate Anthropic’s Claude API with MCP and Mage Pro to automate workflows and unlock advanced prompt engineering. This step-by-step guide covers setup, code examples, and best practices for building smarter, AI-powered tools—perfect for developers and teams looking to boost productivity and stay ahead in the world of AI automation.

This article describes how to build an AI system to answer questions about the Great Attractor using Model Context Protocol (MCP). In part 1 you’ll learn how to use Mage Pro to create a data pipeline that connects knowledge sources to AI systems, including fetching PDF data, cleaning the text, and preparing it for us with Antrhopic’s Claude. This approach allows for more accurate, source-specific AI responses rather than relying solely on general training data.

This guide demonstrates how to integrate your Mage Pro environment with Visual Studio Code and Cursor. By implementing Tailscale VPN and Remote SSH connections, you'll gain the ability to develop and manage your data pipelines directly within your preferred code editor, enhancing your development workflow and productivity. The integration provides a coding experience with full access to your Mage Pro features while leveraging the advanced editing capabilities of modern code editors.

SQL Blocks let you connect to data warehouses like BigQuery with minimal setup while maintaining the flexibility to write custom SQL. You can run either raw SQL (for multiple statements) or simple SQL queries, create/merge tables, and monitor execution output. Key best practices include organizing code, testing queries before deployment, and using version control. Common issues involve connection errors, SQL syntax problems, and data export failures - all of which can be resolved by checking configurations and logs.

This article guides you through the deployments and version control applications in Mage Pro. Both applications simplify how data engineers perform CICD. It explains how to connect Mage Pro to GitHub, use version control features to manage code changes, and configure deployment stages. The platform streamlines the entire workflow from repository setup to deployment, eliminating manual processes and ensuring consistent versioning across environments.

The guide provides a step-by-step tutorial for integrating Mage Pro SQL blocks with Databricks to create automated data pipelines. It walks through configuring Databricks credentials in Mage Pro, creating a pipeline, adding a data loader block to fetch API data (using golf rankings as an example), securely storing API keys, creating SQL blocks to transfer data to Databricks, and finally verifying the data through queries in Databricks. The integration aims to streamline data workflows by eliminating manual processes and creating a seamless connection between data sources and Databricks for analysis.