Model Context Protocol (MCP) Security: How to Restrict Tool Access Using AI Gateways

The Model Context Protocol (MCP) has traditionally allowed AI agents unrestricted access to all tools on an MCP server, which, while useful for experimentation, poses significant security and efficiency challenges in production environments. This unrestricted access can lead to over-permissioned agents, increasing security risks, and a phenomenon called "Context Rot," where an abundance of tools degrades an AI's ability to select the appropriate tool effectively. To address these issues, the text proposes a gateway-level solution that filters tools through Access Control Lists (ACLs), ensuring agents only have access to the tools they need. This method employs a progressive security model with features such as default-deny policies, role-based access, and credential isolation, leveraging tools like Kong AI Gateway for centralized control and management. This approach not only enhances security by limiting tool exposure but also improves performance by reducing unnecessary context load, ultimately streamlining AI agent operations in complex environments.