One MCP Gateway
for Enterprise AI

An MCP gateway is a control layer between AI clients and MCP servers. Every tool call passes through it, so authentication, access rules, and audit logging apply in one place rather than being configured separately for each client and each server.

Without one, every agent holds its own credentials for every server, and no one can answer which tools ran, for whom, or at what cost.

A proxy forwards MCP traffic without understanding it. A gateway parses the protocol, so it can scope which tools a caller sees, attach the right credentials per upstream server, enforce approval on execution, and log each call with its tool name, latency, and cost.

An MCP server exposes a set of tools. An MCP gateway sits in front of many MCP servers and presents them to clients through one endpoint. Bifrost is both: a client to your upstream servers, and a server to your agents.

Yes. Bifrost is open source under the Apache 2.0 license. You can self-host it and read the full source on GitHub.

Bifrost authenticates to each upstream MCP server with OAuth 2.0, server-level or per-user, and refreshes tokens automatically. Credentials stay in the gateway, never in client config, so rotating a token does not require touching a single agent.

Point the client at one Bifrost URL. It discovers every tool it's permitted to use.

Bifrost supports all three MCP transport types: STDIO for local process execution, HTTP for remote MCP servers, and SSE (Server-Sent Events) for real-time streaming connections. OAuth 2.0 authentication with automatic token refresh is built in.

An MCP gateway solves the problem of runaway tool-calling that can overload internal systems or hit provider API limits. The main objective is to regulate resource consumption while maintaining a smooth developer experience.

• Token and request budgeting: set hard limits on tool calls per team to prevent backend overloads and control costs.
• Automated failover: reroute traffic to secondary servers or models if an MCP connection times out or fails.
• Scale-ready architecture: built to handle thousands of tool calls concurrently without degrading performance.

Code Mode replaces traditional tool calling with AI-generated code that orchestrates multiple tools in a single round-trip. Instead of sending 100+ tool schemas in every request, Code Mode uses four meta-tools for on-demand schema loading. This cuts token usage by 50%+ and reduces LLM calls by 3-4x.

No. By default, Bifrost treats tool calls from LLMs as suggestions only. Your application must explicitly approve and trigger execution via a separate API call. This security-first design prevents unintended actions. Agent Mode with auto-execution is available but requires explicit opt-in configuration.