What is an MCP Gateway?

AI agents are becoming increasingly sophisticated, often needing to interact with a broad set of tools and services to complete tasks — from sending emails to managing customer records or building complex models. But as the number of tools and the list of MCP Servers grows, so does the complexity of managing these integrations.

In his article MCP: What it is and Why it Matters, Addy Osmani introduced the idea of an MCP Gateway — a conceptual architecture that centralizes how AI agents interact with tools via a new emerging standard: the Model Context Protocol (MCP).

This article explores the idea of the MCP Gateway: what it is, why it might matter, the problems it aims to solve, and whether it's truly necessary.

Understanding Model Context Protocol (MCP)

Before we explore the gateway concept, it's important to understand what MCP is.

As explained in A Deep Dive into MCP and the Future of AI Tooling (which also briefly suggests the need for an MCP Gateway), Model Context Protocol (MCP) is an open protocol introduced in late 2024 that defines how AI models interact with tools, data, and APIs. Inspired by the Language Server Protocol (LSP), MCP aims to standardize this interaction by providing models with structured access to external services — including tool interfaces, context memory, and state management.

In short, MCP is a protocol that helps models "understand" how to use tools programmatically, and how to manage ongoing interaction with them in a way that preserves context across multiple steps.

This protocol is a key enabler for autonomous AI agents: it allows them to chain tools together, maintain state between calls, and decide which services to invoke depending on the task at hand.

The Challenges of Using Multiple MCP Servers

In practice, using multiple MCP-enabled tools today is still messy. AI agents must open direct connections to each individual MCP server they want to use. This creates several challenges:

• Increased complexity: Each new tool adds another connection to manage — often with its own context format, authentication flow, and interface quirks.
• Security concerns: Every connection represents a potential security vulnerability. Managing authorization and ensuring proper isolation between tenants becomes harder as the number of tools grows.
• Resource inefficiency: Redundant connections and context duplication can result in unnecessary network and compute overhead — especially in systems operating at scale.
• Contextual drift: Without a shared memory or centralized state, AI agents may struggle to maintain consistent context across tools, leading to brittle or error-prone workflows.
• Lack of standardized orchestration: Routing requests, managing rate limits, applying access controls, or even chaining tools together requires custom agent logic — which varies across implementations and tools.

These problems become more acute as agents interact with more services, across more users, in more complex workflows.

What Is an MCP Gateway?

The MCP Gateway is a proposed solution to these challenges.

Rather than having an AI agent connect to each tool separately, it connects to a single gateway. This gateway serves as a unified interface to all MCP-compliant services and manages the orchestration behind the scenes.

You can think of it as an AI-aware service mesh or proxy — one that's designed specifically for tools built around the Model Context Protocol.

What Does the Gateway Do?

An MCP Gateway might include:

• Routing: Directs requests to the appropriate MCP service based on the task.
• Security: Centralizes authentication, authorization, and context isolation.
• Decision-making: Optionally decides which tool to invoke depending on the request.
• Multi-tenancy: Manages multiple users or agents while ensuring data and context remain isolated.
• Policy enforcement: Applies rate limits, logging, and auditing at a central point.
• Context management: Maintains consistent, persistent context across tool calls.

With this model, an AI agent can simply connect to the gateway and access any tool it needs — without worrying about the details of each integration.

Is an MCP Gateway Just an API Gateway?

At first glance, the MCP Gateway might sound like a traditional API gateway — something that routes requests, enforces policies, and simplifies service-to-service communication. And conceptually, there is overlap.

But there are important differences:

• Audience: Traditional API gateways are designed for human-driven client apps or backend services. MCP Gateways are purpose-built for AI agents as the consumer.
• Context awareness: API gateways route based on path or headers. MCP Gateways may route based on semantic intent, task structure, or even agent memory — often requiring deeper contextual understanding.
• State & orchestration: API gateways are usually stateless. MCP Gateways may actively manage contextual state, cache tool responses, or participate in decision-making logic.
• Tool chaining: API gateways treat each request as isolated. MCP Gateways may support multi-step workflows and context-aware tool chaining.
• Protocol alignment: MCP Gateways are designed to work specifically with the Model Context Protocol, enabling standard interactions across AI-compatible services.

So while the architecture may resemble a familiar gateway or proxy, the function, audience, and responsibilities are meaningfully different.

Whether this divergence is enough to justify a new product category — or just a specialized gateway configuration — is still up for debate.

Real-World Example: Zapier MCP

One of the first mainstream implementations of this concept may already be live.

In early 2025, Zapier launched its MCP interface, allowing AI assistants to connect to thousands of applications via a single endpoint. Their implementation offers:

• Simplified connectivity: One interface to access a vast app ecosystem.
• Prebuilt actions: Over 30,000 workflows available out of the box.
• Security features: Authentication, rate-limiting, endpoint isolation.
• Developer customization: Fine-grained control over tool exposure.

Zapier's MCP effectively acts as a gateway between AI agents and thousands of SaaS tools — making it easier for AI to trigger actions in real-world systems.

However, the question remains whether Zapier MCP interactions are purely uni-directional. Can AI Agents can't get the results from actions triggered within Zapier? If Zapier is mostly asynchronous, how do the callbacks from actions get fed back to the AI Agent client?

So, whilst Zapier MCP is powerful, it may currently only offer fire-and-forget type functionality.

Questions and Considerations

The idea of an MCP Gateway is compelling — but not without trade-offs. Important questions include:

• Is an MCP Gateway necessary? Does the added infrastructure justify the benefits? Or is it over-engineering for most use cases?
• Where should orchestration live? Should AI agents manage their own tool logic? Or should this responsibility shift to external gateways?
• How flexible should it be? Should gateways be “dumb routers” or smart decision-makers with logic of their own?
• Will it standardize? Will the ecosystem converge on shared context formats, tool schemas, and gateway protocols?

Final Thoughts

The MCP Gateway is a thoughtful response to a growing need: making AI agent integration more scalable, secure, and manageable. It promises to reduce complexity while improving flexibility — especially in multi-tool, multi-agent, and multi-tenant environments.

But whether it becomes a foundational piece of AI infrastructure or simply a transitional abstraction depends on the evolution of AI development patterns.

One thing is clear: as AI agents become more powerful and autonomous, we need better infrastructure to support their interactions. The MCP Gateway may not be the final answer — but it's asking the right questions.