Flagged: sudo usage. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by HKUDS · MCP Server · ★ 691
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AnyTool safe to install? View the security audit →
AnyTool: Universal Tool-Use Layer for AI Agents ✨ One Line of Code to Supercharge any Agent with Fast, Scalable and Powerful Tool Use ✨ 🎯 What is AnyTool? AnyTool is a Universal Tool-Use Layer that transforms how AI agents interact with tools. It solves three fundamental challenges that prevent reliable agent automation: overwhelming tool contexts, unreliable community tools, and limited capability coverage -- delivering the first truly intelligent tool orchestration system for production AI agents. 💡 Research Highlights ⚡ Fast - Lightning Tool Retrieval Smart
| Stars | 691 |
| Forks | 93 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.979739652911/100 |
| Open Issues | 4 |
| Last Updated | 2026-02-28 |
| Created | 2025-12-09 |
| Platforms | mcp, python |
| Est. Tokens | ~57k |
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AnyTool is "AnyTool: Universal Tool-Use Layer for AI Agents". It is categorized as a MCP Server with 691 GitHub stars.
AnyTool is primarily written in Python. It covers topics such as ai-agents, computer-use-agents, mcp.
You can find installation instructions and usage details in the AnyTool GitHub repository at github.com/HKUDS/AnyTool. The project has 691 stars and 93 forks, indicating an active community.
AnyTool is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to AnyTool on Agent Skills Hub include agent-desktop, llm-functions, forgemax. 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: