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 Omar-V2 · MCP Server · ★ 321
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-ical safe to install? View the security audit →
MCP iCal Server 🗓️ Natural Language Calendar Management for macOS 🌟 Overview Transform how you interact with your macOS calendar using natural language! The mcp-ical server leverages the Model Context Protocol (MCP) to turn your calendar management into a conversational experience. ✨ Features 📅 Event Creation Transform natural language into calendar events instantly! Supported Features Custom calendar selection Location and notes Smart reminders Recurring events Power User Examples text 🔄 Recurring Events: "Set up my weekly team sync every Monday at 9 AM with a 15-minute reminder" 📝 Detailed Events: "Schedule a product review meeting tomorrow from 2-4 PM in the Engineering calendar,
| Stars | 321 |
| Forks | 70 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.8031572539832/100 |
| Open Issues | 24 |
| Last Updated | 2025-04-21 |
| Created | 2025-02-09 |
| Platforms | mcp, python |
| Est. Tokens | ~6k |
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mcp-ical is A Model Context Protocol Server that allows you to interact with your MacOS Calendar through natural language.. It is categorized as a MCP Server with 321 GitHub stars.
mcp-ical is primarily written in Python. It covers topics such as icalendar, llm, mcp.
You can find installation instructions and usage details in the mcp-ical GitHub repository at github.com/Omar-V2/mcp-ical. The project has 321 stars and 70 forks, indicating an active community.
mcp-ical is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-ical on Agent Skills Hub include mysql_mcp_server, atlassian-mcp-server, maverick-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.
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