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 jonnyzzz · MCP Server · ★ 78
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-steroid safe to install? View the security audit →
MCP Steroid Give AI the whole IntelliJ, not just the files AI agents can finally SEE your IDE — not just read code Website • Demo Videos • Blog Post • Discord About MCP Steroid MCP Steroid is an open-source Model Context Protocol server that runs inside JetBrains IDEs and exposes the full power of the IntelliJ Platform to AI agents. Unlike file-only
| Stars | 78 |
| Forks | 21 |
| Language | Kotlin |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 72.1574102802701/100 |
| Open Issues | 147 |
| Last Updated | 2026-09-16 |
| Created | 2026-01-26 |
| Platforms | mcp |
| Est. Tokens | ~19k |
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mcp-steroid is Give your AI Agent a whole IDE, not just the files. It is categorized as a MCP Server with 78 GitHub stars.
mcp-steroid is primarily written in Kotlin. It covers topics such as agent-skill, agent-skills, agent-tools.
You can find installation instructions and usage details in the mcp-steroid GitHub repository at github.com/jonnyzzz/mcp-steroid. The project has 78 stars and 21 forks, indicating an active community.
mcp-steroid is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to mcp-steroid on Agent Skills Hub include skills, aqua, ai-agent-tools-catalog. 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: