by LnYo-Cly · MCP Server · ★ 435
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 435 |
| Forks | 55 |
| Language | Java |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 64/100 |
| Open Issues | 4 |
| Last Updated | 2026-10-13 |
| Created | 2024-08-17 |
| Platforms | cli, java, mcp |
| Est. Tokens | ~17k |
ai4j 一款面向 JDK 8+ 的 Java AI Agentic 开发套件:统一的大模型接入、Tool Calling、MCP、A2A、RAG、Agent Runtime,以及内置的 Coding Agent CLI / TUI / ACP。从基础模型调用到完整的 agent
These tools work well together with ai4j for enhanced workflows:
Looking for a ai4j alternative? If you're comparing ai4j with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
ARGO is an open-source AI Agent platform that brings Local Manus to your desktop. With one-click model downloa
Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent t
Shell and coding agent on mcp clients
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in differen
Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compati
Redis Vector Library. The AI-native Python client for Redis.
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: