"browser" matching MCP servers:

Browser use using Typesafe's Jev model

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 MCP clients to automate a real browser by stating one outcome at a time in natural language, with a decision model (Jev) choosing each element, action, and value while code drives Playwright. Also exposes tools to open pages, verify questions, read page content, snapshot elements, act directly, and call a page's WebMCP tools.

Enables governed browser automation for AI agents with human-in-the-loop approval, audit logging, and read-only replay, supporting both ASK and AUTO autonomy modes.

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.

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 MCP clients to run complete browser workflows through a single run_workflow tool, with anti-detection backends, intelligent element location, automatic retries, and session persistence.

Enables MCP clients to automate a Playwright browser by having an LLM specify step outcomes while a decision model selects elements, actions, values, and statuses, with optional WebMCP tool invocation.

Enables an MCP client to drive a real browser by stating one outcome at a time, while a Typesafe System One model decides which element, action, and value to use and whether the step is done, blocked, errored, or irreversible, with Playwright executing each action. It also lets callers take over directly for page snapshots, question-targeted reads, screenshots, raw element actions, and invoking the page's own WebMCP tools without any model call.

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

Enables LLM clients like Claude to drive a real Playwright browser by stating one outcome at a time, while a TypeSafe Jev model decides which element, action, and value to use each round, returning a status of done, stuck, or needs-confirmation. Also exposes tools for direct element actions, page checks, snapshots, text reading, screenshots, and calling a page's own WebMCP tools.

Enables controlling a real Chromium browser via MCP tools using structural text snapshots instead of screenshots, supporting both step-by-step interaction and fully autonomous task execution.

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 coding agents to control browsers and computers via calibrated judgments from a System One model, with code-based control loops and no vision model.

Enables agents to drive a real logged-in Chrome through parallel background tabs, using decision-model-selected actions and elements while a smarter agent plans goals.

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.