Model Context Protocol (MCP)
// Definition
An open standard introduced by Anthropic in late 2024 that lets AI assistants connect to external tools and data sources through a uniform JSON-RPC interface. An MCP server exposes tools (callable functions), resources (readable data), and prompts (templates) to any MCP-compatible host (Claude Desktop, Claude Code, IDE plugins). Build a server once and any compliant client can use it — the protocol is model-agnostic, which makes integrations portable across AI providers.
// Related terms
Playwright MCP
The official MCP server from Microsoft's Playwright team that gives AI assistants browser automation capabilities. The assistant calls structured tools (browser_navigate, browser_click, browser_type, browser_snapshot) over the protocol; the server drives a real browser via Playwright. Used for AI-driven exploratory testing, bug reproduction, test scaffolding, and debugging — augmenting rather than replacing deterministic Playwright test suites.
AI Testing
The use of AI — language models, machine-learning classifiers, and AI-powered platforms — to accelerate testing tasks: generating test code from descriptions, analysing logs and stack traces, suggesting edge cases, healing broken locators, comparing screenshots intelligently, and triaging failures. AI augments QA engineers; it does not replace the judgement, exploration, and risk-modelling work that humans still do best.
Prompt Engineering
The craft of writing inputs to AI tools — language models, chat assistants, coding assistants — so that the output is useful, specific, and aligned with the task. Core principles include being specific about format, providing project context (existing patterns, conventions, examples), asking for chain-of-thought reasoning, enumerating edge cases up front, and iterating across multiple turns rather than expecting a perfect first response.
Learn more · Playwright MCP
Chapter 1 · Lesson 1: What Is the Model Context Protocol (MCP)