Best MCP Gateways to Connect Tools and MCP Servers to Your AI Agent

AI agents are only as capable as the tools they can access. While Anthropic's Model Context Protocol (MCP) has standardized how agents discover and invoke external tools (from databases and file systems to APIs and SaaS platforms) connecting agents directly to dozens of MCP servers quickly becomes unmanageable in production. Authentication sprawls, observability disappears, and a single misconfigured server can expose sensitive data.

MCP gateways solve this by sitting between your agents and tool servers, providing a single governed entry point for every tool invocation. They centralize authentication, enforce access policies, add audit trails, and deliver the observability needed to understand what agents are actually doing with your tools.

This guide evaluates the top 5 MCP gateways for connecting tools and MCP servers to production AI agents, based on governance capabilities, performance, tool management, and developer experience.

Why Your AI Agents Need an MCP Gateway

Running MCP servers directly works for prototypes, but production deployments expose three critical gaps that gateways are designed to close:

• Security vulnerabilities: Each MCP server executes with whatever permissions you grant it. As your tool ecosystem grows, managing authentication, role-based access, and security boundaries across dozens of servers becomes a liability
• Observability black holes: Direct MCP connections provide zero insight into which tools agents invoke, what data they access, or where failures occur. Without structured logging and tracing, debugging agent behavior becomes guesswork
• Operational chaos: Each server needs its own deployment, monitoring, versioning, and maintenance. Multiply this across development, staging, and production environments, and overhead spirals fast

An MCP gateway eliminates these gaps by routing all tool invocations through a single control plane with consistent security, observability, and management policies.

1. Bifrost by Maxim AI

Bifrost takes a fundamentally different approach to MCP gateway architecture. Rather than treating MCP as an isolated capability requiring separate infrastructure, Bifrost integrates it as a native feature of a high-performance AI gateway, giving teams unified control over both model access and tool invocations through a single platform.

MCP capabilities:

• Centralized tool connections: Connect all MCP servers (filesystem, databases, web search, custom tools) through a single gateway endpoint, eliminating the need for agents to manage multiple server connections
• Tool filtering per virtual key: Control exactly which MCP tools each agent, team, or customer can access through virtual key configurations, preventing unauthorized tool invocations at the infrastructure layer
• Federated authentication: Enterprise deployments support MCP with federated auth, enabling per-user OAuth flows and shared service accounts with centralized credential management
• Governance and audit trails: Every tool call is logged with full metadata through comprehensive audit logging, providing complete visibility into agent-tool interactions for compliance and debugging
• Zero-config tool setup: Define MCP clients via Web UI or JSON config, Bifrost automatically injects available tools into model requests, extending agent capabilities without application code changes

What sets Bifrost apart is the unified gateway architecture. Because Bifrost handles both LLM routing and MCP tool access, teams get a single control plane for model providers, tool servers, budgets, guardrails, and observability. There is no need to deploy and manage a separate MCP proxy alongside your LLM gateway. Integration with Maxim's observability platform extends this further by capturing complete agent execution traces (including every tool call, decision point, and model interaction) for end-to-end evaluation and production monitoring.

Performance: Built in Go, Bifrost adds just 11 µs overhead at 5,000 RPS, ensuring that tool governance never becomes a bottleneck even under heavy concurrent agent workloads.

Best for: Teams that want MCP governance integrated directly into their AI gateway without managing separate infrastructure, and organizations that need tool-level access control, budget enforcement, and observability from a single platform.

2. Kong AI Gateway

Kong AI Gateway extends Kong's established API management platform to support MCP traffic, bringing familiar enterprise governance patterns to AI tool access.

MCP capabilities:

• MCP traffic governance: Route and manage MCP server connections through Kong's existing policy engine with rate limiting, authentication, and access controls
• Plugin-based security: Apply Kong's ecosystem of plugins for request transformation, logging, and security enforcement on MCP traffic
• PII sanitization: Automatically redact sensitive information before tool invocations reach MCP servers
• Unified API and AI management: Manage traditional REST APIs and MCP endpoints through a single Kong control plane

Best for: Enterprises already running Kong for API management that want to extend existing governance infrastructure to AI agent tool access without adopting a new platform.

3. ContextForge (IBM)

ContextForge is an open-source MCP gateway originally developed with contributions from IBM. It positions itself as a feature-rich gateway, proxy, and MCP registry that federates multiple services under a single interface.

MCP capabilities:

• Multi-server federation: Aggregate multiple MCP servers, REST APIs, and agent-to-agent services into a single MCP-compliant endpoint that agents interact with
• Multi-tenant workspaces: Provide different teams with isolated tool catalogs, role-based access boundaries, and independent policy configurations
• Safety plugins: Over 30 built-in plugins for PII detection, content filtering, rate limiting, and policy enforcement applied as pre- and post-hooks on every MCP request
• REST-to-MCP conversion: Automatically expose existing REST APIs as MCP-compatible tools behind the gateway with authentication and rate limiting

Best for: Large enterprises with complex, multi-team environments that need sophisticated tool federation and are comfortable managing open-source infrastructure.

4. Docker MCP Gateway

Docker's open-source MCP Gateway treats MCP servers as container workloads, applying container-native orchestration patterns to AI tool management.

MCP capabilities:

• Container-based isolation: Each MCP server runs in its own container with strict resource limits and network policies for security isolation
• Unified endpoint: Aggregates multiple containerized MCP servers behind a single endpoint for simplified agent connectivity
• Secrets management: Built-in credential handling for MCP servers using Docker's native secrets infrastructure
• Observability hooks: Enterprise-ready logging and monitoring integrated with container orchestration tools

Considerations: Docker MCP Gateway is focused on server orchestration and isolation rather than comprehensive governance. It lacks virtual key management, budget controls, and the granular tool filtering available in gateways like Bifrost.

Best for: DevOps teams already using Docker for infrastructure that want container-native MCP server management with strong isolation guarantees.

5. LiteLLM

LiteLLM provides MCP gateway capabilities as an extension of its open-source LLM proxy, offering basic tool access management alongside its multi-provider routing.

MCP capabilities:

• MCP gateway support: Route MCP tool requests through LiteLLM's proxy with team-based and key-based access controls
• Tool access by team and key: Define which MCP tools are available to specific teams or API keys with granular permissions
• Budget integration: Apply existing LiteLLM budget and rate limit controls to MCP tool usage
• Multi-provider compatibility: Manage MCP tools alongside 100+ LLM provider connections through a single proxy

Considerations: LiteLLM's Python-based architecture introduces performance overhead at scale. Benchmarks show P99 latency reaching 90.72 seconds at 500 RPS compared to Bifrost's 1.68 seconds on identical hardware, a significant concern when agents make dozens of tool calls per conversation.

Best for: Python-first teams that need basic MCP tool management alongside LLM proxy capabilities and are comfortable with performance trade-offs at higher throughput.

How to Choose the Right MCP Gateway

Selecting the right MCP gateway depends on your team's priorities and existing infrastructure:

• Unified vs. standalone: If you already need an LLM gateway for model routing and failover, a unified platform like Bifrost that handles both model access and MCP tools eliminates operational overhead. Standalone MCP proxies require managing separate infrastructure
• Tool-level governance: Production deployments need granular control over which agents access which tools. Look for virtual key-based tool filtering that enforces access policies at the infrastructure layer
• Observability depth: Understanding agent behavior requires visibility into every tool invocation. Gateways that integrate with evaluation and observability platforms enable teams to trace, debug, and continuously improve agent-tool interactions
• Performance at scale: Agents executing multi-step workflows may trigger dozens of tool calls per conversation. Gateway overhead compounds with each call, making low-latency architectures critical for responsive agent experiences
• Authentication model: Enterprise environments need federated auth with per-user OAuth, SSO, and centralized credential management, not just shared API keys

Conclusion

As AI agents evolve from simple chatbots to autonomous systems that execute real-world actions, MCP gateways have become essential infrastructure for secure, observable, and manageable tool access. Among the available solutions, Bifrost by Maxim AI stands out by integrating MCP gateway capabilities directly into a high-performance AI gateway, providing unified governance over both model providers and tool servers, with granular access control, complete audit trails, and native integration with Maxim's observability and evaluation platform.

Whether you are connecting your first MCP server or federating tools across a large organization, centralizing tool access through a governed gateway is the most reliable path to building production-grade AI agents.