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 concierge-hq · MCP Server · ★ 531
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is concierge safe to install? View the security audit →
Concierge AI 🚀 The fabric for reliable MCP servers and AI applications. The Model Context Protocol (MCP) is a standardized way to connect AI agents to tools. Instead of exposing a flat list of every tool on every request, Concierge progressively discloses only what's relevant. Concierge guarantees deterministic results and reliable tool invocation. Getting Started [!NOTE] Concierge requires Python 3.9+. We recommend installing with uv for faster dependency resolution, but pip works just as well. Scaffold a new project: Or wrap an existing MCP server two lines, not
| Stars | 531 |
| Forks | 99 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 69.5595503650591/100 |
| Open Issues | 55 |
| Last Updated | 2026-06-09 |
| Created | 2025-10-19 |
| Platforms | browser, mcp, python |
| Est. Tokens | ~419k |
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concierge is 🚀 Universal SDK for building next-gen MCP servers. It is categorized as a MCP Server with 531 GitHub stars.
concierge is primarily written in Python. It covers topics such as agentic-ai, agentic-web, agentic-web-protocol.
You can find installation instructions and usage details in the concierge GitHub repository at github.com/concierge-hq/concierge. The project has 531 stars and 99 forks, indicating an active community.
The top alternatives to concierge on Agent Skills Hub include flow-like, agentic-radar, agents. 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: