Understanding MCP: How the Model Call Protocol Works and Its Practical Use in AI Tool Integration

Recently I have been preparing data for an AI product demo and many friends asked about MCP, so I take some time to explain MCP from the perspective of a practitioner who actually builds AI applications.

Opinion: “MCP is good, but it is just a protocol.” Many popular articles focus on the vision rather than concrete scenarios.

This article does not repeat the basic concepts of MCP; instead it focuses on answering three questions: What is it useful for? , How to use it? , and Should you use it? .

I prepared a minimal core implementation of MCP that can be understood in five minutes.

Only the code needed for illustration is shown here; the rest is mainly logic handling and calls. To get the full code, follow the public account and send the keyword mcpdemo .

MCP Core Logic

We run MCP locally using the Stdio mode, which means we use StdioServerTransport and StdioClientTransport for the server and client.

After the demo runs, we see that the server provides two simple tools: addition and subtraction. The client successfully retrieves these tools.

We then pose a question: Calculate 1+1 .

The client’s core three‑step logic is:

Call the AI’s function‑call capability; the AI decides whether to use a tool and which one.

Send the chosen tool and parameters to the server, which executes the API and returns the result.

Based on the result, the client calls the AI again to generate the final answer.

Step 1 – AI decides which tool to use

Client code:

const response = await this.openai.chat.completions.create({
    model: model,
    messages,
    tools: this.tools, // important: this.tools is the tool list returned by the server
  });

The code shows that MCP still relies on function call; the claim that “MCP has nothing to do with function call” is false. MCP simply uses the function‑call ability to invoke tools, although you could also decide based on prompts.

MCP is just a protocol. It does not give the large model any new capability, nor does it replace function call.

“MCP does not improve the model’s tool‑calling ability.”

In production, function call faces issues such as tool‑calling accuracy, parameter extraction accuracy, and multi‑intent recognition.

Step 2 – Send tool and parameters to the server for API execution

Client code:

const result = await this.mcp.callTool({
  name: toolName,
  arguments: toolArgs,
});

Server code:

server.tool(
  "加法",
  "计算数字相加",
  {
    "a": z.number().describe("加法的第一个数字"),
    "b": z.number().describe("加法的第二个数字"),
  },
  async ({ a, b, c }) => {
    console.error(`服务端: 收到加法API，计算${a}和${b}两个数的和。模型API发送`);
    // simulate API call
    let data = a + b;
    return {
      content: [
        {
          type: "text",
          text: a + '+' + b + '的结果是：' + data,
        },
      ],
    };
  }
);

Running an MCP server incurs costs; paid products provide MCP support, while free ones rely on community contributions.

Examples of paid MCP integrations include Baidu Map API and Alibaba’s Baijian platform.

Step 3 – Client uses the result to call the AI again

Client code:

messages.push({
  role: "user",
  content: result.content,
});

const aiResponse = await this.openai.chat.completions.create({
  model: model,
  messages: messages,
});

The result is added to messages , and the AI generates the final reply.

So, what is the real benefit of using MCP instead of implementing the flow yourself?

The conclusion is that MCP does not fundamentally change the workflow; it merely standardizes the protocol, which could help interoperability between different companies.

Differences Between Self‑Implementation and MCP

Self‑implementation steps:

Prompt engineers write prompts for each tool.

Backend engineers write model‑calling code using those prompts.

Expose an API for the frontend.

Frontend sends a query; backend uses AI to select a tool.

Backend calls the tool’s API, streams results back to the user.

MCP workflow steps:

Prompt engineers write prompts for each tool.

Backend engineers implement MCP server and client.

MCP client exposes an API for the frontend.

Frontend sends a query; MCP client calls AI to select a tool.

Client sends the chosen tool and parameters to the server, which executes the API and returns the result.

Client calls AI again to generate the final answer.

“In essence, there is no difference.”

Because the function‑call parameter format is already standardized, MCP adds little beyond naming and packaging.

The Real Meaning of MCP

We are still in a chaotic era where AI product pipelines are highly uncertain. MCP offers a technical standard that could help unify integration, but the driving force behind it is ecosystem control and industry influence.

If MCP becomes a widely accepted standard, it could lock in Anthropic’s position in the AI tool‑calling landscape.

Conclusion

My strategy: use MCP for community‑shared tools, but feel free to implement your own solution for internal use.

Implementing MCP yourself deepens your understanding of model applications and AI product development.

Keep experimenting so you can make informed choices when new tools appear.

☺️ Hello, I am “华洛” . If you are interested in transitioning from programmer to AI product lead, please give me a like. Now on 【稀土掘金】 , I will share many experiences and pitfalls from the past three years.

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