Integrating MCP Tools and RBAC into AI Agents: Implementation with LangChain + PyCasbin
Published:Dec 25, 2025 08:05
•1 min read
•Zenn LLM
Analysis
This article discusses implementing Role-Based Access Control (RBAC) in LLM-powered AI agents using the Model Context Protocol (MCP). It highlights the security risks associated with autonomous tool usage by LLMs without proper authorization and demonstrates how PyCasbin can be used to restrict LangChain ReAct agents' actions based on roles. The article focuses on practical implementation, covering HTTP + SSE communication using MCP and RBAC management with PyCasbin. It's a valuable resource for developers looking to enhance the security and control of their AI agent applications.
Key Takeaways
Reference
“本記事では、MCP (Model Context Protocol)を使用して、LLM駆動のAIエージェントに RBAC（Role-Based Access Control）による権限制御を実装する方法を紹介します。”