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 DIDA-AI · MCP Server · ★ 81
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Dida-Hotel-MCP-Global safe to install? View the security audit →
Dida Hotel MCP — Hotel Search & Booking 🏠 Apply Key · 🚀 Quick Start · 📚 Examples · 💬 Support · 🔍 Q&A · ✈ About Dida · 💰Earn with Dida This is an official MCP server empowers AI Agents to search, compare, and book over 2 Million hotels globally. Powered by DIDA (Asia's #1 and world's #3 B2B travel platform), this server bridges the gap between AI travel recommendations and real-world bookings. Transport Protocol: Pricing: Completely free, no usage limits Access Method:
| Stars | 81 |
| Forks | 3 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.7699831714471/100 |
| Last Updated | 2026-09-08 |
| Created | 2026-07-07 |
| Platforms | claude-code, codex, mcp, python |
| Est. Tokens | ~18k |
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Dida-Hotel-MCP-Global is Official DIDA Hotel Booking MCP Server. 14-year travel tech data stack, 2M+ hotels at wholesale rates, 40+ LLM compatible. Free unlimited calls for businesses & individual devs. Filter by location, da. It is categorized as a MCP Server with 81 GitHub stars.
Dida-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 Dida-Hotel-MCP-Global GitHub repository at github.com/DIDA-AI/Dida-Hotel-MCP-Global. The project has 81 stars and 3 forks, indicating an active community.
Dida-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 Dida-Hotel-MCP-Global on Agent Skills Hub include google-analytics-mcp, mcp-fusion, 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: