"Chrome DevTools - Browser Developer Tools & Debugging Resources" matching MCP servers:

An MCP server that enables AI agents to control a real Chrome browser with full login state through 37 structured automation tools, connecting via the Playwriter extension without needing a remote debugging port.

Enables AI agents to control your installed Google Chrome through MCP or HTTP using compact text snapshots, exact pixel coordinates, and a visible cursor. It provides browser tools for navigation, clicking, typing, scrolling, tabs, forms, waiting, recording, and human handoff.

Enables safe automation of Chrome browser through a local MCP server and Chrome extension, allowing LLMs to control browser tabs, pages, and computer-use actions with permission controls.

Enables AI agents to control a real, persistent Chrome browser over MCP, perceiving pages as accessibility trees and interacting with elements by their accessible names without automation fingerprints or site-specific glue.

Enables local Chrome DOM access through MCP for reading visible page text, operating DOM controls, and managing tabs without screenshots or screen sharing.

Enables the Pi coding agent to drive Chrome through DevTools, providing browser automation tools, persistent or isolated profiles, screenshots, page analysis, and bundled agent policies.

Enables AI clients to drive a chosen account's isolated Chrome instance through MCP tools, covering profile discovery and start/stop, navigation, clicking, form filling, text extraction, screenshots, and queued multi-step jobs with result polling, plus manual handover for logins and CAPTCHAs. Also supports session and storage-state export, profile backups, and human takeover/resume when a task needs verification.

Enables Claude Code and Claude Desktop to control your own Chrome browser, with Jev deciding each click, keystroke, and scroll.

Browser use using Typesafe's Jev model

Enables agents using Computer Use to inspect expected browser JavaScript dialogs and respond to OK/Cancel confirmations or text prompts without user intervention. Unexpected dialogs still interrupt the workflow.

Enables coding agents to drive a real Chrome browser to complete natural-language web tasks and return evidence for verification.

Self-hosted MCP server for AI browser automation. Connects to your own Chromium instance via CDP, providing tools for browser control, navigation, interaction, and content extraction.

Gives an AI assistant a persistent, stealth-configured Chrome browser that stays logged in across sessions, supports interactive first login, and can autofill credentials from the macOS Keychain and replay WebAuthn passkeys for sites that block scripted login.

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.

MCP server for AI browser automation with tools for navigation, actions, data extraction, and scripting, supporting local and cloud execution.

Enables AI agents to navigate the web visually using screenshot-based interaction and Set-of-Mark labeling for interactive elements. It supports humanized browsing behaviors, anti-detection measures, and complex tasks like multi-click CAPTCHA solving.

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

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 AI agents to control a persistent Chrome browser through a local daemon, with Jev performing routine actions and the host agent handling reasoning, text, and verification.

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.