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 yjacquin · MCP Server · ★ 1.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is fast-mcp safe to install? View the security audit →
Fast MCP 🚀 Connect AI models to your Ruby applications with ease No complex protocols, no integration headaches, no compatibility issues – just beautiful, expressive Ruby code. 🌟 Interface your Servers with LLMs in minutes AI models are powerful, but they need to interact with your applications to be truly useful. Traditional approaches mean wrestling with: 🔄 Complex communication protocols and custom JSON formats 🔌 Integration challenges with different model providers 🧩 Compatibility issues between your app and AI tools 🧠 Managing the state between AI interactions and your data Fast MCP solves all these problems by providing a clean, Ruby-focused implementation
| Stars | 1,191 |
| Forks | 107 |
| Language | Ruby |
| Category | MCP Server |
| License | MIT |
| Quality Score | 69.4836581867647/100 |
| Open Issues | 66 |
| Last Updated | 2025-12-15 |
| Created | 2025-03-10 |
| Platforms | mcp, ruby |
| Est. Tokens | ~31k |
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fast-mcp is A Ruby Implementation of the Model Context Protocol. It is categorized as a MCP Server with 1.2k GitHub stars.
fast-mcp is primarily written in Ruby. It covers topics such as ai, llm, mcp.
You can find installation instructions and usage details in the fast-mcp GitHub repository at github.com/yjacquin/fast-mcp. The project has 1.2k stars and 107 forks, indicating an active community.
fast-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to fast-mcp on Agent Skills Hub include klavis, lemonade, code-graph-rag. 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: