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 justrach · MCP Server · ★ 1.4k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is codedb safe to install? View the security audit →
codedb Code intelligence server for AI agents. Zig core. MCP native. Zero dependencies. Structural indexing · Trigram search · Word index · Dependency graph · File watching · MCP + HTTP A context engine, not an editor. codedb helps agents find and understand code — search, symbols, callers, dependencies, outlines — and hands editing back to your native tools. codedb has no
| Stars | 1,382 |
| Forks | 87 |
| Language | Zig |
| Category | MCP Server |
| License | BSD-3-Clause |
| Quality Score | 66.421567960198/100 |
| Open Issues | 4 |
| Last Updated | 2026-09-19 |
| Created | 2026-03-03 |
| Platforms | gemini, mcp |
| Est. Tokens | ~21k |
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codedb is Zig code intelligence server and MCP toolset for AI agents. Fast tree, outline, symbol, search, read, edit, deps, snapshot, and remote GitHub repo queries.. It is categorized as a MCP Server with 1.4k GitHub stars.
codedb is primarily written in Zig. It covers topics such as agentic-ai, agents, anthropic.
You can find installation instructions and usage details in the codedb GitHub repository at github.com/justrach/codedb. The project has 1.4k stars and 87 forks, indicating an active community.
codedb is released under the BSD-3-Clause license, making it free to use and modify according to the license terms.
The top alternatives to codedb on Agent Skills Hub include sdk-python, Unity-MCP, tools. 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: