MCP for QA: Build AI-Powered Tools with TypeScript

Build production-ready MCP servers using TypeScript to power AI-driven QA automation.

What you’ll learn
Understand Model Context Protocol (MCP) from first principles
Build an MCP server from scratch using the official TypeScript SDK
Connect an MCP server to a real MySQL database for live data interaction
Build MCP Resources to expose read-only contextual data safely to AI models
Apply Zod schemas to validate MCP tool inputs and outputs like a professional
Handle edge cases and error scenarios

Requirements
Basic JavaScript or TypeScript knowledge
Basic understanding of backend concepts such as What APIs or services are etc

Description
Build production-ready MCP servers using TypeScript to power AI-driven QA automation. Learn MCP fundamentals, create custom tools and resources, integrate real backend systems, and design agentic AI workflows that let LLMs interact with your applications safely and intelligently.AI models like ChatGPT and Claude are powerful, but on their own they can only generate text. To unlock their real potential in software testing and automation, they need a safe and structured way to interact with real systems. That is exactly what Model Context Protocol (MCP) provides — and this course teaches you how to use it effectively for QA and automation.In this course, you will learn how to build production-ready MCP servers using TypeScript, enabling AI models to read data, execute actions, and reason over real backend systems such as databases.Through hands-on, step-by-step lessons, you’ll create custom MCP tools for CRUD operations, expose read-only resources for AI context, and design agentic AI workflows where the model can make decisions, take actions, and request follow-up operations autonomously.This course is ideal for QA engineers, SDETs, automation engineers, and backend developers who want to move beyond traditional automation and build AI-powered testing and validation systems using modern AI protocols.Overall, the course teaches how to move beyond traditional automation and build production-ready, AI-integrated QA systems using modern AI protocols and real-world backend integrations.

Who this course is for
QA Engineers wanting to move into AI-driven testing
SDETs & Automation Engineers
Test Leads & QA Architects
Backend developers curious about MCP
Anyone who wants to build AI-powered tools using real backend systems

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