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 RollingGo-AI · MCP Server · ★ 86
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is RollingGo-Hotel-MCP-Global safe to install? View the security audit →
RollingGo Hotel MCP — Hotel Search & Booking 🏠 Apply Key · 🚀 Quick Start · 📚 Examples · 💬 Support · 🔍 Q&A · 💰Earn with RollingGo This is an official MCP server empowers AI Agents to search, compare, and book over 2 Million hotels globally; this server bridges the gap between AI travel recommendations and real-world bookings. Transport Protocol: Pricing: Completely free, no usage limits Access Method: Self-service following this documentation; suitable for rapid prototyping and tool developme
| Stars | 86 |
| Forks | 19 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.6518180326046/100 |
| Last Updated | 2026-09-22 |
| Created | 2026-08-05 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~18k |
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RollingGo-Hotel-MCP-Global is Official RollingGo Hotel Booking MCP Server. 2M+ hotels at wholesale rates, 40+ LLM compatible. Free unlimited calls for businesses & individual devs. Filter by location, date, star grade, guests & ta. It is categorized as a MCP Server with 86 GitHub stars.
RollingGo-Hotel-MCP-Global is primarily written in Python. It covers topics such as agentic-workflows, ai-agents, booking-engine.
You can find installation instructions and usage details in the RollingGo-Hotel-MCP-Global GitHub repository at github.com/RollingGo-AI/RollingGo-Hotel-MCP-Global. The project has 86 stars and 19 forks, indicating an active community.
RollingGo-Hotel-MCP-Global is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to RollingGo-Hotel-MCP-Global on Agent Skills Hub include Dida-Hotel-MCP-Global, Dida-RollingGo-Hotel-MCP-Global, google-analytics-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: