I Created An Enterprise MCP Gateway

When you start building AI applications beyond simple experiments, everything changes. Models need access to files, databases, APIs, and internal services. That's where the Model Context Protocol (MCP) comes in. But managing dozens of MCP servers, tools, and integrations in production quickly becomes a nightmare. I spent the last few months building an enterprise MCP gateway using Bifrost , and I want to share what I learned. 💻 The Problem: MCP Without a Gateway is Bad Here's what happens without proper infrastructure: Your models spend precious tokens discovering available tools. Teams can't control who uses what. An engineer accidentally deletes the wrong database because the model had access it shouldn't have. API costs spike unexpectedly. You have no idea which AI workflows are running where. The root issue: MCP was designed for flexibility . When you scale from a chatbot to production AI systems, you need: Centralized tool management instead of scattered MCP servers Fine-grained a