MCP Gateway

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, has become the de facto standard for connecting AI models to external tools, data sources, and services. As agentic AI workflows move into production, engineering teams need more than a basic MCP client integration. They need a production-grade

The Model Context Protocol (MCP), introduced by Anthropic in late 2024, has become the de-facto standard for AI agent-to-tool connectivity. Now adopted by OpenAI, Microsoft, and Google, MCP defines how LLM applications discover and interact with external tools, data sources, and workflows through a standardized interface. But

TLDR: MCP (Model Context Protocol) gateways are now essential infrastructure for teams running LLMs in production. This roundup covers the top 5 platforms - Bifrost, Cloudflare, Vercel, LiteLLM, and Kong AI ranked by capability, developer experience, and production readiness. What Is an MCP Gateway? An MCP gateway sits between your application

TL;DR Bifrost's Code Mode reduces token usage by 50%+ and execution latency by 40-50% when working with multiple MCP servers. Instead of loading hundreds of tool schemas, it lets AI write TypeScript to orchestrate tools programmatically, enabling faster execution and lower costs. The Token Bloat Problem

AI agents are only as capable as the tools they can access. While Anthropic's Model Context Protocol (MCP) has standardized how agents discover and invoke external tools (from databases and file systems to APIs and SaaS platforms) connecting agents directly to dozens of MCP servers quickly becomes unmanageable

Build secure, production-grade AI agents with enterprise-level tool orchestration As AI applications evolve beyond simple chat interfaces, developers face a critical challenge: how do you enable AI models to interact with external tools, databases, and APIs safely and efficiently? Bifrost's Model Context Protocol (MCP) Gateway solves