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 doITmagic · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is rag-code-mcp safe to install? View the security audit →
RagCode MCP - Make Your Codebase AI-Ready The privacy-first MCP server that transforms any repository into an AI-ready codebase with semantic search and RAG. RagCode is a Model Context Protocol (MCP) server that instantly makes your project AI-ready. It enables AI assistan
| Stars | 51 |
| Forks | 6 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.489661982253/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-17 |
| Created | 2025-11-23 |
| Platforms | claude-code, go, mcp |
| Est. Tokens | ~50837k |
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rag-code-mcp is Privacy-first semantic code navigation MCP server using RAG. Features deep AST multi-language support (Go, PHP/Laravel/WP, JS/TS/React, Python), 100% local LLMs (Ollama), and vector search (Qdrant) fo. It is categorized as a MCP Server with 51 GitHub stars.
rag-code-mcp is primarily written in Go. It covers topics such as claude-desktop, code-navigation, copilot.
You can find installation instructions and usage details in the rag-code-mcp GitHub repository at github.com/doITmagic/rag-code-mcp. The project has 51 stars and 6 forks, indicating an active community.
rag-code-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to rag-code-mcp on Agent Skills Hub include ShibaClaw, pdf-mcp, pitlane-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: