"playwright" matching MCP servers:

Automated Playwright E2E test repair powered by a self-improving, governed MCP server that runs failing tests, collects failure artifacts, reasons about root causes, validates and applies fixes, and re-runs to verify.

Enables AI coding assistants to generate, score, run, and auto-heal Playwright E2E tests while enforcing domain-driven page object architecture and anti-flakiness rules.

Enables migration of test automation projects from WebDriverIO to Playwright using AST-based transformations. Provides tools for analyzing tests, converting syntax, refactoring to Page Object Model, and generating migration reports.

Analyzes scroll-triggered animations on any webpage using Playwright, extracting GSAP, ScrollTrigger, Lenis, and CSS code via AST and runtime detection.

Enables critique-only analysis of Playwright test-suite hygiene through MCP tools that scan a repository, review pasted code snippets, explain individual rules, and list rules—always returning findings, open questions, and disclaimers rather than an approval.

Enables live pub.dev search, official Flutter/Dart references, static project diagnostics, workflow generation, dependency vulnerability scanning, and an SVG-Edit + Playwright icon workflow through MCP.

Serves a deterministic UI knowledge graph over MCP so coding agents can query verified Playwright locators, routes, screens, actions, and transitions instead of re-reading the DOM. Enables test generation from role, label, and framework-native attributes without data-testid attributes or destructive clicks.

Local MCP server for static Python/TypeScript code audits, Playwright test generation, and SDLC health checks with a 0-100 quality score.

Lets AI agents ask which tests a Playwright or Vitest project contains, which are skipped and why, by reading the test source files statically. Nothing is executed, written, or sent over the network, and answers come back as paged JSON with summaries, filters, and scan diagnostics.

Enables coverage-driven test generation for RHDH plugin forests by analyzing git diffs and coverage reports, recommending the cheapest test layer, and emitting actionable test briefs, including Playwright MCP prompts for UI gaps.

Lets a local AI agent scan a repository to map user flows against their Playwright end-to-end coverage, stand up and tear down a Docker-based local test environment, and write or run the missing specs. It also embeds video, trace, and step-list evidence into a local viewer synced to the flow graph.