TL;DR
If you're building agentic applications in 2026, you need an MCP gateway that can route tools and context across multiple LLM agents without blowing up your token budget. This post breaks down the 5 best MCP gateways available right now, compares their MCP-specific capabilities, and shows you how to get started with one in under a minute.
Quick pick: Bifrost stands out for sub-3ms MCP latency, Code Mode (50%+ token reduction), and four connection types. It is open-source and built in Go.
Why MCP Gateways Matter for Developers in 2026
Model Context Protocol (MCP) changed how developers build AI-powered tools. Instead of hardcoding tool definitions into your prompts, MCP lets AI models discover and execute external tools at runtime. Your model can read files, query databases, search the web, and call APIs, all through a standardized protocol.
But here is the problem. When you are running multiple MCP servers (filesystem, web search, databases, custom APIs), things get messy fast. Token counts explode because every tool definition gets stuffed into the context window. Latency adds up. Security becomes a headache. You lose control over which tools are available to which request.
That is where MCP gateways come in. They sit between your application and your MCP servers, handling tool discovery, filtering, execution, and security in one place. Think of them as reverse proxies, but for MCP tool calls.
If you are evaluating MCP gateways right now, here are the 5 best options worth looking at.
1. Bifrost (by Maxim AI)
GitHub: git.new/bifrost | Docs: getmax.im/bifrostdocs | Website: getmax.im/bifrost-home
Bifrost is an open-source LLM gateway built in Go. It started as a high-performance routing layer for LLM API calls, but its MCP integration is what makes it stand out in 2026.
MCP Features:
- Code Mode: This is Bifrost's biggest differentiator. Instead of sending 100+ tool definitions to the LLM (eating up your context window), Code Mode has the AI write TypeScript to orchestrate multiple tools in a sandboxed environment. The result: 50%+ token reduction and 40-50% lower execution latency compared to classic MCP.
- Agent Mode: Autonomous tool execution with configurable auto-approval. You define which tools can run automatically and which need human oversight.
- Four connection types: InProcess (~0.1ms), STDIO (~1-10ms), HTTP, and SSE. Each is optimized for different deployment patterns. See the MCP architecture docs for details.
- Dynamic tool discovery: Tools are discovered at runtime, not hardcoded. Use the list MCP clients API to see available tools. Tool discovery runs at ~100-500 microseconds (cached after first request).
- Request-level tool filtering: Control exactly which MCP servers and tools are available per request using HTTP headers. Tool filtering runs at ~50-200 nanoseconds per tool.
- Security-first design: Tool calls from LLMs are treated as suggestions only. Execution requires a separate API call unless you explicitly enable Agent Mode. Per-tool rate limiting and guardrails are built in.
- MCP Server mode: Bifrost can also expose your connected tools as an MCP server, so clients like Claude Desktop, LibreChat, or Codex CLI can connect directly.
Works with: Claude Code, LibreChat, Codex CLI, Qwen Code
Strengths:
- Sub-3ms MCP latency
- Open-source (Go), self-hostable with easy setup
- Code Mode is a genuine innovation for multi-tool workflows
- Granular per-request tool access control via virtual keys
Limitations:
- InProcess connections require Go (cannot be configured via JSON)
- Younger ecosystem compared to some alternatives
Verdict: If you are running 3+ MCP servers and token costs are a concern, Bifrost's Code Mode alone makes it worth trying. The fact that it is open-source and self-hostable is a bonus.
2. OpenRouter
OpenRouter is a unified API gateway that lets you access models from OpenAI, Anthropic, Google, Meta, and dozens of other providers through a single endpoint. It has expanded into MCP routing as part of its broader AI infrastructure play.
MCP Features:
- MCP tool routing through its unified API layer
- Model-agnostic tool execution across 200+ models
- Usage-based pricing with per-tool cost tracking
- Built-in fallback routing if a model fails a tool call
- Community-shared tool definitions and configurations
Strengths:
- Widest model coverage of any gateway
- Simple API with minimal setup
- Pay-per-use pricing (no upfront commitment)
- Active community and model leaderboard
Limitations:
- Not open-source (cloud-only service)
- No equivalent to Code Mode for token reduction
- MCP support is layered on top of its model routing, not a core architectural feature
- Limited request-level tool filtering compared to dedicated MCP gateways
- No self-hosting option
Verdict: If you need access to a wide range of models and want basic MCP tool routing through one API, OpenRouter is convenient. But if MCP orchestration is your primary concern, you will find the tooling less specialized.
3. Cloudflare AI Gateway
Cloudflare AI Gateway leverages Cloudflare's edge network to proxy and manage AI API calls. It provides caching, rate limiting, and analytics for LLM requests.
MCP Features:
- MCP request proxying through Cloudflare's edge network
- Built-in caching for tool responses at the edge
- Rate limiting per tool or per user
- Analytics dashboard for tracking MCP usage
- Integration with Cloudflare Workers for custom logic
Strengths:
- Edge network gives low latency globally
- Strong DDoS protection and rate limiting out of the box
- Easy integration if you are already on Cloudflare
- Good analytics and monitoring
Limitations:
- Tied to Cloudflare's ecosystem
- No Code Mode or advanced token optimization for MCP
- Limited tool filtering granularity compared to dedicated MCP gateways
- MCP support is secondary to its core AI gateway features
- Not self-hostable (cloud-only)
Verdict: Good if your infrastructure is already on Cloudflare and you want basic MCP proxying with edge caching. Not the best choice if you need deep MCP-specific features.
4. Kong AI Gateway
Kong is an established API gateway that has extended into AI territory. Kong AI Gateway adds LLM-specific features on top of Kong's proven API management platform.
MCP Features:
- MCP routing through Kong's plugin architecture
- Rate limiting and authentication via existing Kong plugins
- Request/response transformation for MCP payloads
- Integration with Kong's service mesh for internal MCP servers
- Plugin ecosystem for custom MCP logic
Strengths:
- Mature API gateway with battle-tested reliability
- Extensive plugin ecosystem
- Strong enterprise support and documentation
- Good fit if you already run Kong for API management
Limitations:
- MCP is handled through plugins, not native architecture
- Configuration complexity (Kong's learning curve applies)
- No MCP-specific optimizations like Code Mode or dynamic tool discovery
- Heavier resource footprint for MCP-only use cases
- Open-source core, but AI features may require enterprise license
Verdict: If you are an enterprise already running Kong and want to route MCP traffic through the same infrastructure, it makes sense. For MCP-first use cases, it is overkill with less specialized tooling.
5. LiteLLM Proxy
LiteLLM is an open-source proxy that provides a unified interface for 100+ LLM providers. It has added MCP routing capabilities that allow you to proxy tool calls alongside regular LLM requests.
MCP Features:
- MCP tool call proxying through the unified LLM interface
- Provider-agnostic tool routing
- Basic tool call logging and tracking
- Virtual key management for access control
- Open-source and self-hostable
Strengths:
- Wide provider support (100+ LLMs)
- Open-source with active community
- Lightweight and easy to set up
- Good if you need LLM routing + basic MCP
Limitations:
- MCP support is basic compared to dedicated MCP gateways
- No Code Mode, no advanced token optimization
- No request-level tool filtering
- Python-based (higher latency than Go-based alternatives for MCP operations)
- Limited MCP connection type support
Verdict: Good for teams that need a lightweight, open-source LLM proxy with basic MCP capabilities. If MCP tool orchestration is your primary concern, you will outgrow it quickly.
Comparison Table
| Feature | Bifrost | OpenRouter | Cloudflare AI GW | Kong AI GW | LiteLLM Proxy |
|---|---|---|---|---|---|
| Open Source | Yes (Go) | No | No | Partial | Yes (Python) |
| MCP Latency | Sub-3ms | Not published | Edge-dependent | Not published | Not published |
| Code Mode (token reduction) | Yes (50%+) | No | No | No | No |
| Agent Mode | Yes | No | No | No | No |
| Connection Types | 4 (InProcess, STDIO, HTTP, SSE) | HTTP | HTTP | HTTP (via plugins) | HTTP |
| Dynamic Tool Discovery | Yes (~100-500 microseconds) | Limited | No | No | Limited |
| Request-Level Tool Filtering | Yes (~50-200ns per tool) | Limited | Limited | Via plugins | No |
| Per-Tool Rate Limiting | Yes | Yes | Yes | Yes (via plugins) | Limited |
| Security Scanning | Yes | Yes | Yes | Via plugins | No |
| Self-Hostable | Yes | No | No | Yes | Yes |
| MCP Server Mode | Yes | No | No | No | No |
Quick Setup: Bifrost MCP in 60 Seconds
Here is a quick example showing how to use Bifrost's MCP features with a single curl command. Follow the setup guide and provider configuration first, then send a chat completion request while filtering which MCP clients and tools are available.
Step 1: Include only specific MCP clients in your request:
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "x-bf-mcp-include-clients: filesystem,websearch" \
-d '{
"model": "gpt-4o-mini",
"messages": [
{
"role": "user",
"content": "List all files in the current directory"
}
]
}'
Step 2: Or filter down to specific tools:
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "x-bf-mcp-include-tools: filesystem/read_file,websearch/search" \
-d '{
"model": "gpt-4o-mini",
"messages": [
{
"role": "user",
"content": "Read the contents of config.json"
}
]
}'
The key thing to notice: tool filtering happens at the request level via HTTP headers. You don't need to reconfigure the gateway. Each request can specify exactly which MCP servers and tools it has access to.
For the full setup guide, check the Bifrost MCP documentation. You can also explore semantic caching for repeated tool calls, observability for monitoring MCP traffic, and custom plugins for extending MCP behaviour.
So Which MCP Gateway Should You Pick?
It depends on what you are building.
If MCP is your primary use case and you care about latency, token costs, and granular tool control, Bifrost is the strongest option right now. Code Mode alone can cut your MCP token usage in half, and the sub-3ms latency with four connection types gives you flexibility that other gateways do not offer. Plus, it is open-source, written in Go, and supports drop-in replacement for existing OpenAI-compatible setups.
If you want a managed cloud solution and are already on Cloudflare, their AI Gateway handles basic MCP proxying with good edge performance.
If you are already running Kong or OpenRouter, their MCP add-ons will work for basic tool routing without adding another piece of infrastructure.
If you want open-source and lightweight, LiteLLM gives you basic MCP alongside its LLM proxy features, but you will miss out on advanced MCP capabilities.
For most developers building agentic applications with multiple MCP servers in 2026, the choice comes down to whether you need basic MCP proxying or full MCP orchestration. If it is the latter, start with Bifrost.
Found this useful? Star the Bifrost repo on GitHub and check out the docs to get started.
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