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 kuberstar · MCP Server · ★ 65
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is qartez-mcp safe to install? View the security audit →
Qartez MCP X-ray vision for your codebase - built for AI agents, not humans. The first code-intelligence server designed from day one to be consumed by language models, not read by people. Cuts AI token usage by 94%. Quickstart · 37 Tools · Guard · Benchmarks · Comparison · 37 Languages · CLI · Contributing · Security · Changelog Why this exists , , , and were invented in the 1970s for humans reading one file at a time in a terminal. Half a century later, your AI assistant is still usi
| Stars | 65 |
| Forks | 5 |
| Language | Rust |
| Category | MCP Server |
| Quality Score | 70.435413019087/100 |
| Open Issues | 2 |
| Last Updated | 2026-07-28 |
| Created | 2026-04-14 |
| Platforms | claude-code, mcp, rust |
| Est. Tokens | ~33k |
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qartez-mcp is Semantic code intelligence MCP server for Claude Code - project maps, symbol search, impact analysis, and more. It is categorized as a MCP Server with 65 GitHub stars.
qartez-mcp is primarily written in Rust. It covers topics such as claude-code, code-intelligence, code-intelligence-mcp.
You can find installation instructions and usage details in the qartez-mcp GitHub repository at github.com/kuberstar/qartez-mcp. The project has 65 stars and 5 forks, indicating an active community.
The top alternatives to qartez-mcp on Agent Skills Hub include fossil-mcp, tree-sitter-analyzer, roslyn-codelens-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.
Sources & who's responsible: