Web Automation with Playwright and Python using AI and MCP

Web automation testing has shifted considerably with the arrival of both Playwright as a testing framework and AI-assisted tooling as a practical force multiplier, and Web Automation with Playwright and Python Using AI and MCP is built around both of those shifts simultaneously. Kailash Pathak covers the full range of testing types that modern web applications require — end-to-end functional testing, API testing, visual regression testing, and accessibility testing — all implemented through Playwright’s Python bindings. What sets this book apart from a standard Playwright guide is its integration of AI tooling and the Model Context Protocol (MCP) into the testing workflow, reflecting how automation engineers are actually starting to work rather than how they worked two or three years ago. AI assistance is treated as a genuine productivity layer for test generation, maintenance, and analysis rather than a novelty feature, and MCP integration opens up possibilities for more dynamic, context-aware automation that static test scripts can’t achieve on their own. At 446 pages, the book covers this ground with enough technical depth to be genuinely useful for automation engineers building real test suites, moving well past toy examples into the kinds of complex, multi-layered testing scenarios that production web applications actually require. For QA engineers and developers who want to build modern, comprehensive automation testing practices using Playwright and Python with AI tooling integrated from the start, this offers a timely and practically grounded resource that most existing automation books don’t yet address.

Author: Kailash Pathak