No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by AgentDeskAI · MCP Server · ★ 7.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is browser-tools-mcp safe to install? View the security audit →
THIS PROJECT IS NO LONGER ACTIVE PLEASE USE A DIFFERENT SOLUTION FOR THIS. BrowserTools MCP Make your AI tools 10x more aware and capable of interacting with your browser This application is a powerful browser monitoring and interaction tool that enables AI-powered applications via Anthropic's Model Context Protocol (MCP) to capture and analyze browser data through a Chrome extension. Read our docs for the full installation, quickstart and contribution guides. Roadmap Check out our project roadmap here: Github Roadmap / Project Board Updates v1.2.0 is out! Here's a quick breakdown of the update: You can now enable "Allow Auto-Paste into Cursor" within the DevTools panel.
| Stars | 7,290 |
| Forks | 527 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 64.9298480769422/100 |
| Last Updated | 2026-08-12 |
| Created | 2025-02-10 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~16k |
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browser-tools-mcp is Monitor browser logs directly from Cursor and other MCP compatible IDEs.. It is categorized as a MCP Server with 7.3k GitHub stars.
browser-tools-mcp is primarily written in TypeScript. It covers topics such as ai, anthropic, cursor.
You can find installation instructions and usage details in the browser-tools-mcp GitHub repository at github.com/AgentDeskAI/browser-tools-mcp. The project has 7.3k stars and 527 forks, indicating an active community.
browser-tools-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to browser-tools-mcp on Agent Skills Hub include Unity-MCP, dbhub, unity-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: