MCP Gateway

An MCP gateway controls which AI agent can reach which tool, under whose identity. This guide compares five MCP gateways for enterprise AI agents, including Bifrost, Kong, and AWS AgentCore, on tool scoping, per-user OAuth, and Code Mode's 92.8% input-token reduction.

MCP gateway routing decides which server handles each tool call; control decides whether the agent may make it. This guide compares Bifrost, Kong, Docker, Microsoft MCP Gateway, and Lasso on both

TL;DR * An MCP gateway is a single control plane between AI models and MCP servers that handles tool discovery, routing, authentication, governance, and execution from one endpoint. * Without a gateway, every agent configures its own MCP servers, credentials, and approval rules, which creates configuration sprawl, token bloat, and no

MCP adoption has outpaced the infrastructure needed to govern it. An MCP governance layer decides which tools each agent can call, who called them, what was returned, and what it cost. This guide covers tool-level access control, the two log types compliance needs, and where token cost actually goes

Code execution with MCP has the model write one Python script that calls many tools in a sandbox. Bifrost Code Mode cut input tokens by up to 92.8% at 508 tools with a 100% pass rate.

This post compares five enterprise MCP governance platforms on those controls, with Bifrost ranked first for per-key tool filtering, guardrails on tool execution, and audit coverage.