Using an MCP Gateway with Claude Code: How Bifrost Centralizes Tool Access for Agentic Coding

TL;DR: Claude Code is a powerful terminal-based coding agent, but managing multiple MCP servers alongside it creates tool sprawl and security gaps. Bifrost, the open-source LLM gateway by Maxim AI, acts as an MCP gateway that centralizes tool connections, governance, and observability for Claude Code. Route all your Claude Code traffic through Bifrost with a single environment variable change, and gain multi-provider model switching, centralized MCP tool management, budget controls, and full request logging.

Claude Code has quickly become the go-to terminal agent for AI-assisted development. It reads your codebase, executes commands, edits files, and creates pull requests from a single CLI session. Pair it with MCP (Model Context Protocol) servers, and Claude Code can also query databases, search the web, interact with issue trackers, and access filesystems.

But a problem surfaces fast: every MCP server you add to Claude Code is a standalone connection. Each one brings its own credentials, its own configuration, and zero centralized visibility into what tools the model is actually invoking. For solo developers, that is manageable. For teams running production AI workflows, it becomes a governance headache.

This is where Bifrost fits in as an MCP gateway layer between Claude Code and your tool infrastructure.

What Is an MCP Gateway, and Why Does Claude Code Need One?

MCP is an open standard that lets AI models discover and execute external tools at runtime. Claude Code natively supports MCP through HTTP, SSE, and stdio transports. You can add servers using the claude mcp add command and start using tools immediately.

The challenge is scale. When you have three or four MCP servers, each exposing 10-20 tools, the model's context window fills up with tool definitions before it starts reasoning about your actual task. Token costs go up, latency increases, and the model wastes effort understanding what tools exist rather than solving your problem.

An MCP gateway solves this by acting as a single control plane between Claude Code and all your MCP servers. Instead of connecting directly to each server, Claude Code connects to the gateway, which handles tool discovery, routing, authentication, and execution centrally.

Bifrost treats MCP as a native capability of the gateway rather than a bolt-on feature. This means LLM routing, tool management, budget controls, and observability all live in one place.

Setting Up Claude Code with Bifrost

Getting started takes under a minute. First, install and run Bifrost:

npx -y @maximhq/bifrost
# Or use Docker
docker run -p 8080:8080 maximhq/bifrost

Then configure Claude Code to route through Bifrost by setting two environment variables:

export ANTHROPIC_API_KEY=dummy-key
export ANTHROPIC_BASE_URL=http://localhost:8080/anthropic

All Claude Code traffic now flows through Bifrost. The integration setup automatically detects whether you are using an Anthropic MAX account or standard API key authentication.

Configure your providers and MCP servers through Bifrost's web UI at http://localhost:8080. Any MCP tools you register with Bifrost, such as filesystem access, database queries, or web search, become transparently available to Claude Code without any client-side changes.

What You Get with Bifrost as Your MCP Gateway

Centralized tool governance. Every MCP tool invocation passes through Bifrost, creating a single audit trail. You control which tools are available through virtual key policies, so different teams or projects can have different tool access levels.

Multi-provider model switching. Bifrost translates between provider API formats, letting you switch Claude Code's underlying model to GPT-4, Gemini, or any other configured provider. Claude Code sends Anthropic-formatted requests; Bifrost converts them to the target format and translates responses back seamlessly.

/model vertex/claude-haiku-4-5
/model openai/gpt-4o

Budget controls and rate limiting. Set spending limits per team, project, or virtual key using Bifrost's governance layer. Useful when multiple developers are running concurrent Claude Code sessions.

Full request observability. Bifrost logs every request and response flowing through the gateway. The built-in dashboard at http://localhost:8080/logs shows token consumption, tool usage patterns, and latency breakdowns. For deeper production monitoring, Bifrost integrates natively with Maxim's observability platform, capturing full agent execution traces including tool calls and model decisions.

Suggest, don't execute. Bifrost follows a security-first model where tool calls are suggested but not auto-executed. Your application reviews and approves each execution before it runs. This is especially critical when tools have write access to production systems.

When This Setup Makes Sense

Not every Claude Code user needs a gateway. If you are a solo developer running a single MCP server for filesystem access, connecting directly works fine.

But the moment your setup involves multiple MCP servers, shared team environments, budget constraints, or any production-adjacent workflow, a gateway layer becomes essential. The overhead Bifrost adds is negligible: just 11 microseconds per request at 5,000 RPS. Your infrastructure layer never becomes the bottleneck.

For teams already using Maxim for AI evaluation and agent observability, Bifrost provides a natural extension. Gateway logs feed directly into Maxim's tracing and quality monitoring workflows, closing the loop between development-time coding sessions and production reliability.

Getting Started

Bifrost is open source under Apache 2.0 and deploys with zero configuration. The quickstart guide covers setup in detail, and the Claude Code integration docs walk through the complete configuration.

Adding Bifrost as a gateway to your Claude Code workflow is a small configuration change that delivers meaningful control over tool access, cost, and visibility. And as your AI workflows grow more complex, having that centralized control plane already in place saves significant rearchitecting later.