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 golf-mcp · MCP Server · ★ 840
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is golf safe to install? View the security audit →
⛳ Golf Easiest framework for building MCP servers 📚 Documentation Overview Golf is a framework designed to streamline the creation of MCP server applications. It allows developers to define server's capabilities—tools, prompts, and resources—as simple Python files within a conventional directory structure. Golf then automatically discovers, parses, and compiles these components into a runnable MCP server, minimizing boilerplate and accelerating development. With Golf v0.2.0, you get enterprise-grade authentication (JWT, OAuth Server, API key, development tokens), built-in utilities for LLM interactions, and automatic telemetry integration. Focus on implementing your agent's logic while Golf handles authentication,
| Stars | 840 |
| Forks | 70 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 73.5311907720481/100 |
| Open Issues | 2 |
| Last Updated | 2026-09-09 |
| Created | 2025-02-24 |
| Platforms | mcp, python |
| Est. Tokens | ~15k |
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golf is Production-Ready MCP Server Framework • Build, deploy & scale secure AI agent infrastructure • Includes Auth, Observability, Debugger, Telemetry & Runtime • Run real-world MCPs powering AI Agents. It is categorized as a MCP Server with 840 GitHub stars.
golf is primarily written in Python. It covers topics such as agent-runtime, ai, ai-agent.
You can find installation instructions and usage details in the golf GitHub repository at github.com/golf-mcp/golf. The project has 840 stars and 70 forks, indicating an active community.
golf is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to golf on Agent Skills Hub include arcade-mcp, agents, openops. 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: