As companies' cloud infrastructures expand, the need for accessible data integration platforms like Fivetran becomes crucial. Fivetran's simplicity allows users to quickly establish new connections, but scalability issues arise when dealing with numerous connections across various teams and environments. To address this, Fivetran proposes the use of a Model Context Protocol (MCP) server that enables AI assistants to manage Fivetran through natural language, enhancing accessibility and governance with features like approval workflows and Google Drive-backed audit trails. MCP standardizes how large language models (LLMs) interact with external tools, allowing for consistent AI integration that translates users' natural language instructions into precise API calls. This approach supports scalable, AI-assisted data pipeline management by enabling stateful workflows and multi-user collaboration while maintaining enterprise governance. The integration also includes a comprehensive approval system using Google Drive for version history and external auditing, ensuring safety and transparency. The AI-assisted infrastructure management project showcases the potential of AI to amplify human capabilities in data engineering, enabling engineers to manage hundreds of connectors efficiently while maintaining security and compliance. The MCP server, currently operational, demonstrates significant improvements in speed and reliability, with plans for further enhancements in dynamic templates, health monitoring, and cross-platform expansion.