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 ascending-llc · MCP Server · ★ 2.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is jarvis-registry safe to install? View the security audit →
Connect any AI copilot or autonomous agent to your enterprise tools — through a single, secure MCP gateway with built-in identity, access control, and full observability. 🚀 Quick Start 🌐 Website What is Jarvis Registry? Jarvis Registry is an open-source, enterprise-grade MCP (Model Context Protocol) and A2A Agent Gateway and Workflow Orchestration platform built by ASCENDING Inc. It solves one of the hardest problems in enterprise AI: giving AI copilots and autonomous agents secure, governed access to internal tools and data — without fragmented integrations or security blind spots.
| Stars | 2,829 |
| Forks | 418 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 72.1930491326499/100 |
| Last Updated | 2026-09-11 |
| Created | 2025-10-04 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
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jarvis-registry is Connect any AI copilot or autonomous agent to your enterprise tools — through a single, secure MCP/Agent gateway with built-in identity, access control, and full observability.. It is categorized as a MCP Server with 2.8k GitHub stars.
jarvis-registry is primarily written in Python. It covers topics such as agent, agent-gateway, agent-orchestration.
You can find installation instructions and usage details in the jarvis-registry GitHub repository at github.com/ascending-llc/jarvis-registry. The project has 2.8k stars and 418 forks, indicating an active community.
jarvis-registry is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to jarvis-registry on Agent Skills Hub include archestra, gateway, claude-context. 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: