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 brightdata · MCP Server · ★ 2.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is brightdata-mcp safe to install? View the security audit →
The Web MCP 🌐 Give your AI real-time web superpowers Seamlessly connect LLMs to the live web without getting blocked Quick Start • Features • Pricing • Demos • Docs • Support 🎉 Free Tier Available! 🎉 5,000 requests/month FREE Perfect for prototyping and everyday AI workflows 🚀 <a href="https://brightdata.com/ai/
| Stars | 2,646 |
| Forks | 327 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66.2610435708306/100 |
| Open Issues | 44 |
| Last Updated | 2026-09-15 |
| Created | 2025-04-15 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~17k |
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brightdata-mcp is A powerful Model Context Protocol (MCP) server that provides an all-in-one solution for public web access.. It is categorized as a MCP Server with 2.6k GitHub stars.
brightdata-mcp is primarily written in JavaScript. It covers topics such as ai-agents, ai-integrations, anti-bot-detection.
You can find installation instructions and usage details in the brightdata-mcp GitHub repository at github.com/brightdata/brightdata-mcp. The project has 2.6k stars and 327 forks, indicating an active community.
brightdata-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to brightdata-mcp on Agent Skills Hub include crw, fastapi_mcp, hexstrike-ai. 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: