When Anthropic introduced the Model Context Protocol (MCP) in November 2024, the problem it was trying to solve was relatively easy to describe.
AI models were becoming increasingly capable, but they were still isolated from the systems where companies actually work. Connecting an assistant to a database, CRM, development environment, or enterprise app usually required a custom integration. Add another agent or another system, and another integration had to be built.
MCP proposed a common interface instead.
Anthropic described it as an open standard for connecting AI systems with external data sources and tools. A little more than a year later, MCP had expanded far beyond the Claude ecosystem. In December 2025, Anthropic donated the protocol to the Agentic AI Foundation under the Linux Foundation, alongside support from companies including OpenAI, Google, Microsoft and AWS.
The protocol has continued to mature. The July 2026 specification moved MCP toward a stateless core designed to make servers easier to scale, route, cache and govern in production infrastructure.
MCP is increasingly showing up in enterprise AI architecture diagrams next to another rapidly growing component: the AI Gateway.
They are often discussed together. They are not the same thing.
The simplest distinction is this:
MCP standardizes how AI accesses tools. An AI Gateway controls how AI traffic is routed, secured, observed and governed.
What MCP Actually Is
One source of confusion is that “MCP” can refer to several different things in the same conversation.
At the highest level, MCP is a protocol.
It defines a standardized way for AI applications to communicate with external capabilities. The protocol establishes how clients and servers exchange messages, discover available capabilities and invoke them.
Within that architecture, three components matter most:
An MCP server can expose capabilities such as tools, resources, and prompts. Tools allow the model to execute functions, while resources provide structured context or data.
Take a procurement agent.
The underlying ERP might already have dozens of APIs for inventory, suppliers, purchase requisitions and approvals. Instead of expecting the agent to understand each API independently, an MCP server could expose a smaller set of business-oriented tools:
- Check_inventory
- Find_approved_supplier
- Create_purchase_requisition
- Check_purchase_order_status
The agent discovers those tools through the MCP interface and invokes the appropriate one.
Importantly, MCP does not replace the underlying API.
The architecture can still look like this:
AI Agent → MCP Client → MCP Server → REST API → ERP
MCP is the AI-facing interface. The API remains the software-facing interface underneath it.
This distinction becomes particularly valuable when several AI applications need access to the same capability. Instead of implementing the SAP inventory API separately for five different agents, the enterprise can expose the capability through one standardized MCP interface.
So where does the AI Gateway fit?
An AI Gateway solves a different problem.
Imagine that the same procurement agent can use GPT, Claude and Gemini. The company wants to choose which model handles each request, manage provider credentials, apply rate limits, observe latency, record usage and potentially route traffic based on cost or performance.
That is traditionally the territory of the AI Gateway.
Modern AI Gateways provide a central layer between applications and AI providers. They can normalize model access, route requests between providers, apply policies and provide centralized visibility into AI traffic.
The two layers therefore answer different architectural questions:
Another way to think about it:
AI Gateway manages access to intelligence.
MCP manages access to capabilities.
A simplified architecture could look like this:
The agent sits in the middle. On one side, it needs models to reason. On the other, it needs tools to act.
The lines are starting to blur
The conceptual distinction remains important, but the product landscape is already becoming more complicated.
AI Gateways are expanding beyond model traffic.
Some platforms now apply the same gateway principles to MCP traffic, providing authentication, authorization, rate limiting, observability and policy enforcement in front of MCP servers.
This produces an architecture closer to:
That does not mean AI Gateway and MCP have become the same thing.
The distinction is similar to HTTP and an API gateway.
HTTP defines how systems communicate. An API gateway governs the traffic flowing through those interfaces.
In the same way:
MCP is the interoperability protocol.
The gateway is the control layer around it.
MCP Is Not AI Governance
Connecting an agent to an enterprise system through MCP does not automatically make that connection secure or well governed.
For an enterprise, protocol-level security is only the beginning.
A production environment also needs to answer questions such as:
- Who owns this MCP server?
- Is the server approved?
- Which agents can access it?
- Which users can invoke a specific tool?
- Should some actions require human approval?
- What data can the tool return?
- What happened during a particular agent execution?
- Which version of the tool was used?
That is AI governance around MCP, rather than MCP itself.
A mature architecture may therefore add several controls:
MCP Registry: Which MCP servers and tools are approved?
Gateway: Who can call them, and under what policies?
Identity: On whose behalf is the agent operating?
Human-in-the-loop controls: Which actions require approval?
Observability: What happened during the interaction?
Audit: Who initiated the action, which agent executed it and what was the outcome?
MCP makes tools interoperable. Governance determines whether they should be used.
MCP Also Creates an AI FinOps Problem
AI FinOps is often reduced to one question: how many tokens are we consuming?
That becomes incomplete once agents start taking actions.
An agentic workflow can generate cost across several layers:
Consider an agent that costs only a few cents in model inference but makes 30 unnecessary tool calls, triggers an expensive data query and retries the same workflow several times.
Looking only at token consumption would miss most of the cost.
MCP therefore does not provide AI FinOps, but it creates an important new source of telemetry for it.
Enterprises increasingly need visibility not just into tokens per request, but into:
- Tool calls per task
- Cost per tool call
- Retries
- Latency
- Failure rates
- Model plus tool cost per completed outcome
That also explains why putting a gateway or observability layer around MCP traffic can become valuable. A company may eventually want to trace one business transaction from the model decision all the way through the tool execution.
The unit of economics becomes the business task, not simply the token.
And Where Does A2A Fit?
MCP solves another problem that is sometimes confused with agent interoperability.
Suppose a procurement agent needs help from a specialized supplier-risk agent.
The procurement agent could theoretically expose the other agent as a tool. But that starts to break down when the interaction becomes more complex.
Perhaps the supplier-risk agent needs to ask follow-up questions, perform several steps, send progress updates, negotiate what work it will perform, return multiple artifacts or maintain a longer-running interaction.
That is where Agent2Agent, or A2A, enters the architecture.
The difference is straightforward:
Consider a supply-chain process:
Procurement Agent
uses MCP → SAP procurement tools
Supplier Risk Agent
uses MCP → risk databases and external data
Procurement Agent
uses A2A → Supplier Risk Agent
The tools remain behind MCP. The agents communicate through A2A.
That separation can become increasingly important as enterprises deploy agents from different vendors.
Is MCP Fundamental for Every Enterprise?
Not necessarily.
The wrong conclusion from MCP’s rapid adoption would be that every API should immediately become an MCP server.
For some workloads, the existing integration is already the right architecture.
A deterministic system-to-system process does not become better simply because an AI agent is added in the middle. High-volume ETL, scheduled integrations and predictable transactional workflows will often remain better suited to APIs, event-driven architectures, integration platforms and conventional automation.
The question is not, “Can we expose this through MCP?”
The better question is, “Does standardizing this capability for AI consumers create enough value to justify it?”
A practical framework looks like this:
The tipping point is usually reuse.
If one agent calls one API, MCP may simply add another layer.
If 20 agents from several platforms need access to hundreds of enterprise capabilities, standardization becomes much more valuable.
Without it, the organization risks building an increasingly complex integration matrix:
many agents × many systems × many custom connectors.
MCP turns that into a more reusable model:
many agents → standardized tool interfaces → enterprise systems.
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