If you lead a platform or infrastructure team that is wiring agents into real systems, this post sets out what the Model Context Protocol buys you and what it doesn’t. MCP replaces the per-pair connector with one integration layer: one server per system and one reusable client-side protocol implementation per agent application. That is the whole point. It does not remove the system-specific work, and it does not reduce the number of live connections you operate. Knowing where that boundary sits tells you where to spend your team’s effort.
The problem it was built for
MCP was created at Anthropic and open-sourced in November 2024. Its creators described the problem as a classic M×N problem: before MCP, every application and database needed custom code to connect with an LLM. In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, which it co-founded with Block and OpenAI. That matters to a platform lead because the protocol is no longer governed by one model vendor.
The arithmetic is simple. In a September 4, 2026 walkthrough, Bryan Krausen uses 3 AI applications and 4 systems. Without a shared protocol, that is 12 custom integrations. With MCP, it becomes 7 standard implementations: 3 application-side implementations plus 4 servers. At 10 applications and 50 systems, as many as 500 custom pairings drop to about 60 implementations.
What actually gets smaller
Read that 7 carefully. Krausen is explicit that it counts implementations of the shared protocol. The MCP architecture uses a one-to-one relationship between a client and a server, so in a fully connected setup all 12 client-server relationships can still exist at runtime. The Enlyft engineering team describes the same model. A host creates and manages clients and enforces security and consent. Each client typically has one connection to one server.
The savings are in code you write and maintain, not in the topology you run. You still have to observe, authenticate and debug every live connection. What you stop doing is writing a different GitHub connector for every agent that needs GitHub.
The second thing that gets smaller is guesswork. MCP standardizes three kinds of capabilities (tools, resources and prompts), and clients can list what a server offers at runtime. Enlyft names “no discovery” as one of the core problems with per-client REST integrations. MCP gives you a standard way for a client to ask a server what it can do.
What I’d do
- Make each MCP server the single owned integration for its system. Enlyft wraps its existing services rather than replacing them. The server translates protocol requests into internal calls, and all real work stays in the existing services. I’d hold every server to that standard. A server that grows its own business logic is a second implementation you now have to keep in sync.
- Put the security work in the server, because that is where access lives. Krausen notes that access to the underlying system remains with the server deployment. He recommends narrow permissions, approval for operations that change or delete something, and OAuth. MCP gives you one place to enforce this per system. It does not enforce it for you.
- Run servers as a platform product, not as side projects of agent teams. This is my judgment rather than anything the sources state. The reuse only pays off if the second and third agents actually use the first server instead of building their own.
Where the point doesn’t apply
If you have one agent talking to one system, there is nothing to reuse. The savings come from sharing integration code across applications, so a single pairing gains little beyond discovery and a standard shape. I wouldn’t adopt MCP for that case on integration grounds alone.
MCP also doesn’t make the hard part of each integration go away. As Krausen puts it, the server still has to understand the underlying API, authentication model and permissions. The protocol standardizes how the agent side connects, not what the system side has to know.
And it is not a pipeline standard for models. The DEV Community piece points out that MCP connects each model to your agent as a tool. It does not define how the output of one model becomes the input of another when their schemas differ. If your problem is chaining specialist models with incompatible formats, MCP is the wrong layer. Don’t expect it to fix that.
Decide which systems get an MCP server first by asking one question: how many agents need this today, or will next quarter? Build servers for the systems where the answer is more than one, keep them thin, and treat them as owned integration code.