"playwright" matching MCP servers:

An MCP server for running Playwright tests and reading structured results, failed test details, and attachment content, designed for AI agents doing test failure analysis.

Enables syncing Playwright, Allure, and JUnit test results into existing Kiwi TCMS test executions, with dry-run safety, automatic failure comments and attachments, and guarded raw RPC access.

Enables agents to run Playwright suites, diagnose failures from trace-level DOM, network and console evidence, and apply locator fixes that are re-run and reverted if still red. It also repeats failing tests for per-test flaky, regressed or broken verdicts and surfaces the same diagnosis and fix diffs in CI job summaries and PRs.

An MCP server that unpacks and structures Playwright trace.zip archives so AI agents can perform root-cause analysis on CI failures, with 16 focused tools for inspection, DOM/UI analysis, root-cause analysis, and performance analysis.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

Compacts noisy test, build, and cloud-log output before an AI agent reads it — dedupes repeats, collapses stack frames, folds Playwright retries, and renders CloudWatch/GCP JSON logs down to the signal. Typically 80–95% fewer tokens on failures. Tool: compact_output. Run: npx -y logslim logslim-mcp

A temporary MCP server that scrapes Bitrise Insights using Playwright to fetch flaky test data, providing tools to query workspace and app test insights until the official API is released.

Enables AI agents to run reusable Playwright test fixtures against live deployments, providing structured test results, screenshots, and assertions.

Read-only Playwright CI results for coding agents: tells whether a failing test is new in this PR, already failing on main, or known flaky, with per-test history.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Connects TestDino test management platform to AI agents, enabling users to check test runs, analyze failures, upload Playwright test results, and manage test cases through natural language commands.

MCP server for end-to-end QA automation: generates test scenarios, discovers Playwright locators, creates TypeScript test code, executes tests, and creates GitHub issues for failures.

Python MCP server for Selenium WebDriver — 84 tools for browser automation, element interactions, assertions, self-healing locators, and codegen for Java TestNG / JUnit 5 / Cucumber / pytest / C# NUnit / Playwright and CI pipelines (GitHub Actions / Jenkins / GitLab CI). No ChromeDriver setup needed.

Enables querying Playwright test failure reports and execution results directly from AI assistants, allowing retrieval of test outcomes, failures, and report details.

A dual-track testing server that combines CLI test execution with Playwright-based browser testing and persistent SQLite logging. It enables automated test pipelines, Git integration, and evidence-based requirement generation to streamline the development lifecycle.

Enables MCP-capable agents to queue and evaluate real ComfyUI workflows through a live instance, returning structured pass/fail results without needing to know Playwright or ComfyUI internals.

MCP server for nopCommerce QA automation using Playwright. Enables running tests, viewing reports, analyzing failures, and generating QA dashboards.

Showcases principal-level QA architecture using MCP tools and Playwright for validating security, business workflows, contract safety, and cross-layer consistency.

Automates repository analysis, test planning, and generation of end-to-end tests with Playwright, acting as an intelligent quality assistant.