"Playwright" matching MCP servers:

Enables AI agents to automate browser testing on the OrangeHRM demo application using natural language prompts, generating and executing Playwright tests with accessibility tree support.

this is my first mcp

Enables AI agents to perform end-to-end web testing by controlling a real browser through tools for navigation, interaction, state inspection, screenshots, and script evaluation.

Enables AI assistants to drive a real browser with human-like pointer and keyboard actions for navigation, form interaction, and page reading. It supports stealth-oriented browsing with optional proxy, persistent profile, and seeded identity settings.

Run real Playwright E2E tests from your AI coding agent.

Enables browser automation through MCP with native Playwright operations, deterministic assertions, and natural-language grounded actions, structured extraction, and bounded agent workflows over live page elements.

MCP middleware that prunes Playwright accessibility snapshots for LLM agents, reducing tokens by 75-95% while preserving all references, enabling agents to interact with web pages efficiently.

Enables remote control of a computer connected to a JetKVM device by capturing its screen and sending mouse, keyboard, and scroll inputs via Playwright and the JetKVM's local Web UI.

Autonomously analyzes projects, generates Playwright tests via OpenAI, runs them, classifies failures, and recommends fixes, iterating up to 3 times.

Enables AI agents to run durable, isolated, and observable browser automation tasks via Playwright, with persistent profiles, recovery, evidence capture, and unattended execution for long-running web workflows.

Token-efficient browser automation for AI agents, filtering the DOM to only interactive elements and grouping them by page section, reducing token usage by 3-13x compared to Playwright MCP.

MCP server for autonomous end-to-end testing with Wopee.io. Analyzes web applications, generates and executes Playwright-based functional tests, and validates results — all driven by natural language commands.

An MCP server that lets LLM agents autonomously play retro games by driving a real OpalEmu emulator in a Playwright-controlled browser, exposing tools for loading ROMs, controlling buttons, and capturing screenshots.

A local stdio MCP server that exposes compact semantic observations from UI elements annotated with data-agent-* hints, designed to work alongside the official Playwright MCP for browser automation.

Playwright for the entire OS. Give AI agents eyes and hands on any desktop app — find, click, type, and read UI elements across Linux, macOS, and Windows.

Enables AI assistants to control a web browser through Playwright-compatible tools, with an on-device decision engine for autonomous task completion and optional LLM escalation.

Website testing MCP server built for AI agents: 63 Playwright tools with hard assertions, auto form-fill, persistent authenticated sessions, network mocking, and accessibility/SEO/GEO + Lighthouse audits.

Testing MCP for coding agents: exercise real user journeys, independently recheck suspected bugs, and export verified findings as Playwright regression tests. Includes browser interaction, goal coverage, evidence-linked reports, and optional local monitoring. The host agent supplies the model and judgment; Argus needs no separate provider key.

Gives AI agents a single shared, logged-in Chrome they can drive over MCP or plain HTTP: open and read tabs, click, type, upload files, run short Playwright scripts, hand off whole web tasks to a cheaper model, and keep per-site memory. Agents work in background windows they own without stealing the user's mouse or closing their tabs.

Enables browser automation in which a planning LLM states one outcome at a time while a Typesafe System One model decides which element, action, and value to use, with Playwright carrying it out. Exposes tools for opening pages, driving multi-step goals, checking or choosing among options, reading relevant page text, acting on elements directly, and calling a page's own WebMCP tools.