AI agents are only as powerful as the tools they can reliably use. Anthropic’s Model Context Protocol (MCP) standardizes how agents discover and invoke external tools - from databases and file systems to APIs and SaaS platforms. But once you move beyond a handful of tools, directly wiring agents to multiple MCP servers becomes brittle and unsafe.
Authentication sprawl creeps in. Observability vanishes. One misconfigured server can expose sensitive systems or data.
MCP gateways address this by sitting between agents and MCP servers, acting as a governed control plane for all tool usage. They centralize authentication, enforce access policies, generate audit trails, and provide the visibility required to understand how agents actually operate in production.
This guide reviews the top 5 MCP gateways for connecting tools and MCP servers to production AI agents, evaluated on governance depth, performance, tool management, and developer experience.
Why Production AI Agents Need an MCP Gateway
Direct MCP server connections may work in prototypes, but they break down quickly in real-world deployments. Gateways are designed to close three core gaps:
- Security risk - Each MCP server runs with its own permissions. As tool count grows, managing auth, RBAC, and blast radius across servers becomes error-prone
- Observability blind spots - Native MCP connections offer no structured insight into which tools were called, what data was accessed, or where failures occurred
- Operational overhead - Every MCP server needs deployment, monitoring, upgrades, and environment parity across dev, staging, and prod
An MCP gateway consolidates all tool traffic through a single control plane, delivering consistent security, visibility, and operational sanity.
1. Bifrost by Maxim AI
Bifrost approaches MCP differently from most gateways. Instead of shipping MCP as a standalone proxy, it treats MCP as a native capability of a high-performance AI gateway. This gives teams unified control over both model access and tool execution from a single platform.
MCP capabilities
- Centralized tool connectivity - Connect all MCP servers (databases, file systems, web search, custom tools) through one gateway endpoint
- Tool filtering by virtual key - Precisely control which tools each agent, team, or customer can access using virtual key policies
- Federated authentication - Support for per-user OAuth flows and shared service accounts with centralized credential management
- Governance and audit logs - Every tool invocation is logged with full metadata for compliance, debugging, and security reviews
- Zero-code tool injection - Configure MCP clients via UI or JSON and automatically expose tools to agents without application changes
What sets Bifrost apart is its unified gateway architecture. Because it manages LLM routing and MCP tools together, teams avoid running parallel infrastructure for models, tools, budgets, and guardrails. When paired with Maxim’s observability platform, Bifrost captures full agent execution traces - including tool calls, decisions, and model interactions - enabling end-to-end evaluation and production monitoring.
Performance
Built in Go, Bifrost adds roughly 11 microseconds of overhead at 5,000 RPS, ensuring tool governance does not become a bottleneck even for highly concurrent agent workloads.
Best for: Teams that want MCP governance embedded directly into their AI gateway, with strong access control, budgets, and observability from a single control plane.
2. Kong AI Gateway
Kong AI Gateway extends Kong’s widely adopted API management platform to support MCP traffic, applying enterprise-grade governance patterns to AI tool access.
MCP capabilities
- Policy-driven MCP routing using Kong’s existing auth, rate limiting, and access controls
- Plugin-based security via Kong’s mature plugin ecosystem
- PII redaction before tool requests reach MCP servers
- Unified API and AI management across REST APIs and MCP endpoints
Best for: Organizations already using Kong that want to reuse existing governance infrastructure for agent tool access.
3. ContextForge (IBM)
ContextForge is an open-source MCP gateway with roots in IBM-led development. It positions itself as a gateway, proxy, and registry that federates multiple services behind a single MCP interface.
MCP capabilities
- Multi-server federation across MCP servers, REST APIs, and agent services
- Multi-tenant workspaces with isolated tool catalogs and RBAC boundaries
- Safety and policy plugins for PII detection, filtering, and rate limits
- REST-to-MCP translation to expose existing APIs as MCP tools
Best for: Large enterprises with complex tool ecosystems that are comfortable operating open-source gateway infrastructure.
4. Docker MCP Gateway
Docker’s MCP Gateway applies container-native patterns to MCP server management, treating each tool server as a containerized workload.
MCP capabilities
- Container isolation for each MCP server with resource and network boundaries
- Single aggregated endpoint for simplified agent connectivity
- Native secrets management using Docker’s credential tooling
- Operational observability integrated with container platforms
Limitations
Docker MCP Gateway focuses on orchestration and isolation rather than fine-grained governance. It lacks virtual key management, budget enforcement, and advanced tool filtering.
Best for: DevOps-driven teams that already rely heavily on Docker and want strong isolation for MCP servers.
5. LiteLLM
LiteLLM offers MCP gateway functionality as an extension of its open-source LLM proxy, combining tool access management with multi-provider routing.
MCP capabilities
- MCP routing through the LiteLLM proxy
- Team- and key-based tool permissions
- Shared budget and rate limits applied to tool usage
- Unified management of MCP tools and LLM providers
Limitations
LiteLLM’s Python-based architecture introduces significant overhead at scale. In high-concurrency scenarios, gateway latency compounds rapidly when agents make multiple tool calls per workflow.
Best for: Python-first teams that want lightweight MCP support bundled with an LLM proxy and can tolerate performance trade-offs.
How to Choose the Right MCP Gateway
When evaluating MCP gateways, focus on:
- Unified vs standalone design - A single gateway for models and tools reduces operational complexity
- Granular tool governance - Production agents require strict control over which tools can be invoked
- Observability depth - Full visibility into tool calls is critical for debugging and evaluation
- Latency at scale - Tool-heavy agents amplify gateway overhead
- Enterprise authentication - Look beyond shared API keys to federated auth and SSO
Final Thoughts
As AI agents transition from assistants to autonomous systems executing real-world actions, MCP gateways become critical infrastructure. Centralizing tool access through a governed gateway is the safest and most scalable approach.
Among current options, Bifrost by Maxim AI stands out by embedding MCP governance directly into a high-performance AI gateway, delivering unified control over models and tools, detailed audit trails, and native integration with observability and evaluation workflows.
Whether you are onboarding your first MCP server or federating tools across an enterprise, an MCP gateway is the foundation for building production-grade AI agents.
Top comments (1)
Great overview. One thing I've noticed running agents in production — the tool discovery problem extends beyond MCP. Agents need to find the right tools in the first place, before you even get to the gateway layer.
I've been building a curated SaaS and AI tools directory at saasrow.com partly to solve this — browsable by category instead of relying on search or word-of-mouth. The idea is that if you can browse what's available (MCP servers, API tools, SaaS platforms), the gateway layer becomes easier to plan.
Bifrost's zero-code tool injection is interesting because it assumes you already know what to connect. The harder problem is knowing what exists. Curious if any of these gateways have plans for tool registries or discovery feeds.