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 ali-kamali · MCP Server · ★ 167
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Axon.MCP.Server safe to install? View the security audit →
Axon.MCP.Server Model Context Protocol (MCP) Server for AI IDEs - Cursor, AntiGravity & Claude Transform your codebase into an intelligent knowledge base for AI-powered development with Cursor IDE, Google AntiGravity, and MCP-enabled assistants []() []() 📋 Table of Contents -
| Stars | 167 |
| Forks | 23 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 63.9994566557582/100 |
| Open Issues | 16 |
| Last Updated | 2026-06-30 |
| Created | 2025-12-26 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~528k |
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Axon.MCP.Server is Transform your codebase into an intelligent knowledge base for AI-powered development with Cursor IDE, Google AntiGravity, and MCP-enabled assistants. It is categorized as a MCP Server with 167 GitHub stars.
Axon.MCP.Server is primarily written in Python. It covers topics such as antigravity, azure-devops, claude.
You can find installation instructions and usage details in the Axon.MCP.Server GitHub repository at github.com/ali-kamali/Axon.MCP.Server. The project has 167 stars and 23 forks, indicating an active community.
The top alternatives to Axon.MCP.Server on Agent Skills Hub include claude-talk-to-figma-mcp, Mantic.sh, octocode. 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: