MCP Tools (Codex AI Coding Agent Tutorial #6)

All Episodes 👉    • OpenAI Codex Full Course: AI Coding Agent ...   AI models are powerful, but they often need access to external knowledge and services to produce reliable results. In this episode, we explore the Model Context Protocol (MCP) and learn how it extends Codex by connecting it to external tools and documentation sources. We use Context7 MCP to provide always up-to-date Python and library documentation during code generation, making AI-assisted development more accurate and practical. We then implement a production-style continuous deployment pipeline by provisioning infrastructure with Terraform, deploying a containerized application to Google Cloud Run, and automating infrastructure updates through GitHub Actions. By the end of the episode, you’ll understand how MCP tools enhance AI workflows and how to build an automated deployment process that keeps your application and infrastructure in sync. Pytest Course: Python Test Automation & GitHub Actions CI/CD 👉 https://www.udemy.com/course/pytest-c... Learn Pytest Framework: Python Automation Testing, Unit Testing, API Testing & Test Automation with GitHub Actions CI/CD Install Terraform 👉 https://developer.hashicorp.com/terra... Install Google Cloud CLI 👉 https://docs.cloud.google.com/sdk/doc... 0:00 Introduction 2:34 Context7 MCP 9:37 MCP Tools Context 13:49 GCP & Terraform Deployment 20:30 Continuous Deployment 23:10 Update Deployment 29:10 Summary