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 rohitsoni007 · MCP Server · ★ 62
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-gearbox safe to install? View the security audit →
MCP Gearbox A modern desktop application for managing Model Context Protocol (MCP) servers across multiple AI agents 📸 Screenshot 📚 Documentation For detailed usage instructions, please visit our documentation. 🤔 What is MCP Gearbox? MCP Gearbox is a powerful cross-platform desktop application that simplifies the management of Model Context Protocol (MCP) servers for AI agents like Claude, Kiro, Continue, Copilot, Cursor, Qoder, and others. Built with Electron, React, and TypeScript, this productivity tool provides an intuitive interface to discover, install, configure, and manage MCP servers without touching configuration files. Whether you're a
| Stars | 62 |
| Forks | 11 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 68.4228105948994/100 |
| Open Issues | 5 |
| Last Updated | 2026-01-27 |
| Created | 2025-11-03 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~158k |
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mcp-gearbox is A modern desktop application for managing Model Context Protocol (MCP) servers across multiple AI agents. It is categorized as a MCP Server with 62 GitHub stars.
mcp-gearbox is primarily written in TypeScript. It covers topics such as ai, ai-agents, ai-tools.
You can find installation instructions and usage details in the mcp-gearbox GitHub repository at github.com/rohitsoni007/mcp-gearbox. The project has 62 stars and 11 forks, indicating an active community.
mcp-gearbox is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-gearbox on Agent Skills Hub include mcp-fusion, claude-historian-mcp, claude-emporium. 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: