What Is MCP (Model Context Protocol)? What Problem It Actually Solves

Say your company uses ten SaaS products, and each one needs to be wired into AI. Platform A connects to Feishu, and Platform B has to rewrite the Feishu integration from scratch; switch large models and everything written before goes to waste. That is the daily reality before MCP (Model Context Protocol): every vendor does its own thing and the duplicated work explodes.

This article first explains “how messy it was,” then “how MCP fixes it,” and finally covers the architecture, the workflow, the three capability primitives, and how it differs from Function Calling — all in one pass, with a few diagrams to follow along.

1. How messy it was: the N×M integration hell

In the old days, every AI platform that wanted to reach an external system had to write its own integration code. It was fine while tools were few; once tools multiplied, it got out of hand: the same calendar written ten times for ten platforms, and switching models meant reconnecting everything. People doing integrations spent most of their time “re-wiring” instead of doing something valuable. I watched a small team burn two weeks just wiring five internal systems into two different AI tools, and that didn’t even include the maintenance afterward.

In engineering this is the N×M problem: N models connecting to M tools/data sources theoretically means writing N×M integrations. Every extra model or data source multiplies the workload instead of adding to it.

✅ MCP: M+N adapters AI models × N MCP standard Write once, use everywhere Tools / sources × M 5+2 = 7 adapters; reuse directly on a model switch

Figure 1: traditional point-to-point integration is a multiplicative relationship of “N models × M tools”; MCP turns it into “M+N” — a tool adapts to the standard once and every model shares it.

2. How MCP fixes it: giving the plug a national standard

It standardizes “AI connecting to tools” into one unified protocol. Tool providers adapt once per the protocol, and every MCP-supporting AI can use it directly, with no per-platform code. Every appliance used to need its own outlet; MCP is like setting a national standard for the plug, so outlets no longer need to change their socket for each appliance.

Key point: MCP governs “how to connect and how to transfer”; it doesn’t decide “what can be done once connected.” The actual work still belongs to Skills and Agents; MCP just stops them from writing a pile of duplicate code. The same five systems, adapted once per MCP, can be used directly by another AI tool, and that two-week job collapses to a day or two.

3. What MCP actually is: the Host / Client / Server three layers

MCP was officially open-sourced by Anthropic in November 2024. Its design is often compared to USB-C: just as USB-C connects many devices through one interface, MCP gives AI applications a “plug-and-play” context framework. Technically, it abstracts the communication between model and external systems into a client-server architecture, passing context and calling tools through standard messages based on JSON-RPC.

The whole system revolves around three roles:

Host: the application that hosts the AI interaction environment, such as Claude Desktop, Cursor, or VS Code. It brokers between the user, the model, and external capabilities, and runs MCP Clients internally.
Client: a component running inside the Host that is dedicated to establishing a connection with a specific MCP Server, sending and receiving requests — the bridge between Host and external resources.
Server: a lightweight program that exposes specific capabilities and data per the standard so the model can call them. One Host can connect to many Servers.

Figure 2: the three-layer MCP architecture. A Host runs a Client, the Client connects to Servers over the standard protocol; the Server opens Tools / Resources / Prompts to the model, and those capabilities touch external data.

4. How it works: client → protocol → server

MCP’s runtime can be understood in three layers. Understand these and you won’t fear writing your own MCP client or server.

1) Client: where requests start

The Client or Host is the origin of the whole interaction — it turns “what the user/model wants” into a standard request and sends it. Any program that wants to connect AI to a database, API, or file system can act as a client.

2) Communication layer: the standard protocol as the bond

This layer uses JSON-RPC to define the uniform structure of requests and responses — like HTTP on the internet. Whether you use Claude or LLaMA, whether the other side is Feishu or an internal store, both sides speak the same language. It also handles authentication, error handling, and data formats to keep communication stable.

3) Server side: the supplier of capabilities and data

The Server receives client requests, performs the concrete operation (query the database, call a tool, read a file), and returns the result to the model in the standard format. It is lightweight and customizable, handling everything from simple data fetching to complex workflows.

Figure 3: a typical call. Solid lines are the request direction, dashed lines are results returning along the same path. Client and Server always exchange unified JSON-RPC messages.

5. The three capabilities a Server can expose: Tools / Resources / Prompts

MCP groups “what can be given to the model” into three primitives. Remember these three and you can basically tell what any MCP Server does.

🛠

Tools

Let the model “take action” — execute concrete operations like querying a database, running code, sending a message, or calling an API. They are the outlet where the model’s language ability lands.

📦

Resources

Feed data to the model — database contents, cloud files, live API data. They strengthen context awareness so output is based on the latest information.

💡

Prompts

Reusable prompt templates and workflows that help developers bake in standard interactions — good for scenarios like customer service and content generation that iterate in batches.

6. How MCP differs from Function Calling and traditional APIs

The easiest thing to confuse is MCP and Function Calling. One-line distinction: Function Calling is the capability of whether the model can call functions; MCP is the protocol for how functions get plugged in standardized, shared by every model. One is a capability on the model side, the other is a standard on the integration side. A tool written for one Server can be used by any MCP-supporting model through Function Calling — they complement each other, they don’t replace each other.

Dimension	MCP (protocol)	Function Calling (capability)	Traditional point-to-point API
Solves	A unified “connection standard”	The mechanism for the model to initiate tool calls	Interfaces between specific systems
Reusability	High: adapt once, shared by many models	Medium: often tied to one model vendor	Low: rewritten for every pair
Integration cost	M+N	Depends on each model’s implementation	N×M
Typical form	Host + Client + Server	Model returns a function_call	Hand-written integration code
Relationship	MCP exposes tools through a standard protocol, models call those tools through Function Calling; traditional APIs are the “messy wiring” state that MCP replaces.

7. How it relates to your site’s Connector / Skill / Agent

This is 运营GO’s own framing. It doesn’t conflict with the above; it just swaps the lens:

Connector is the specific wire; MCP is the standard for what that wire should look like.
Skill can be built on top of MCP — the platform grabs capabilities per the standard instead of rewriting them itself.
Agent orchestrates multiple Skills/tools to finish a task; MCP saves it from writing a pile of duplicate wiring.
For ordinary users, MCP’s biggest win is: the same tool keeps working when you switch to a different AI — you aren’t locked in.

8. A concrete before-and-after

Take that small team again: five internal systems need to connect into two different AI tools. The old way is 5×2 = 10 integrations, each re-done on model upgrades, taking over two weeks and maintaining a pile of soon-outdated code. With MCP, each of the five systems does one standard adapter, the two AI tools reuse them directly, it’s done in about a day, and switching models later needs no reconnection.

Figure 4: integration workload comparison. When tools grow from 5 to 10, traditional integrations go from 10 to 20; MCP only goes from 7 to 12, and those adapters are reused by every model.

9. Its development timeline

2024-11

Anthropic officially open-sources MCP, publishing the spec and Python / TypeScript SDKs, plus prebuilt Servers for Google Drive, GitHub, and more.

Design inspiration

Borrows from USB-C (one unified interface) and LSP (Language Server Protocol) — LSP turned the “editor × language” N×M problem into M+N, and MCP wants to do the same for the AI ecosystem.

From 2025

The community and vendors gradually join in; Hosts (Claude Desktop, Cursor, etc.) and various Servers grow fast, and an interoperable ecosystem takes shape.

10. Summary

One line to close: MCP isn’t another “make the model stronger” magic; it’s a unified standard for how AI connects to the outside world. It compresses N×M repeated wiring into M+N one-time adapters; with the Host / Client / Server three layers plus JSON-RPC, it opens Tools / Resources / Prompts to any model. For integrators it saves work; for AI users it means “switching tools doesn’t cost you your capabilities.”

If you want to see it in action, start with an existing MCP Server (say, one that connects local files or an API), get that call chain running, and only then think about writing your own Server.