MCP Gateway

Code execution with MCP replaces direct tool calls with Python orchestration, cutting AI agent token costs by up to 92.8% while preserving full tool access. Code execution with MCP is the architectural shift that lets AI agents scale across hundreds of tools without burning their context budget on tool

Designing safe AI agent loops means choosing where to gate tool execution. A practical guide to manual approval, autonomous execution, and the middle path. Every production AI agent eventually has to answer the same question: when the model proposes a tool call, who decides whether it runs? The two ends

Bifrost MCP Tool Groups let platform teams govern MCP tool access across teams, customers, users, and providers from a single named policy. Once an MCP deployment moves past a handful of virtual keys, managing tool access one key at a time stops working. A new team needs the same database

MCP RBAC enforces tool-level permissions for production AI agents. Learn how Bifrost's virtual keys and tool filtering deliver least-privilege access at scale. Production AI agents now connect to dozens of Model Context Protocol (MCP) servers exposing hundreds of tools, yet most setups still rely on a

An MCP gateway provides production teams with auth, cost governance, and tool access control. See why direct MCP integrations break at enterprise scale. Production MCP deployments fail in predictable ways. A team wires three Model Context Protocol servers into an agent during prototyping. It works. They add five more for

An MCP gateway centralizes tool discovery, authentication, and governance for AI agents. Learn why production AI stacks now require this layer, and how Bifrost delivers it. The Model Context Protocol (MCP) has become the standard way AI agents talk to tools. With 97 million monthly SDK downloads and over 10,