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 mnemox-ai · MCP Server · ★ 817
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is idea-reality-mcp safe to install? View the security audit →
English | 繁體中文 idea-reality-mcp Your AI agent checks before it builds. Automatically. The only MCP tool that searches 5 real databases before your agent writes a single line of code. No manual search. No forgotten step. Just facts. Works with: Claude Desktop · Claude Code · Cursor · Windsurf · any MCP client Try it in your browser — no install What it does You: "AI code review tool" idea-reality-mcp: ├── realitysignal: 90/100 ├── GitHub repos: 847 ├── Top competitor: reviewdog (9,094 ⭐) ├── npm packages: 56 ├── HN discussions: 254 └── Verdict: HIGH — c
| Stars | 817 |
| Forks | 87 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 77.8984903516218/100 |
| Open Issues | 3 |
| Last Updated | 2026-09-18 |
| Created | 2026-02-24 |
| Platforms | mcp, python |
| Est. Tokens | ~16k |
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idea-reality-mcp is Pre-build reality check for AI coding agents. Scans GitHub, HN, npm, PyPI, Product Hunt. MCP server. 290+ stars.. It is categorized as a MCP Server with 817 GitHub stars.
idea-reality-mcp is primarily written in Python. It covers topics such as ai-agents, developer-tools, github-api.
You can find installation instructions and usage details in the idea-reality-mcp GitHub repository at github.com/mnemox-ai/idea-reality-mcp. The project has 817 stars and 87 forks, indicating an active community.
idea-reality-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to idea-reality-mcp on Agent Skills Hub include atlassian-mcp-server, roam-code, daymon. 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: