🧠 Model Context Protocol (MCP) Detailed explanation: "USB interface in the new era of AI programming

Target readers: Developers who have a certain programming foundation but have not yet been involved in AI programming
Purpose of this article: Helps you understand the core concepts, technical advantages, and operating mechanisms of MCP, and guides you how to use MCP to build an agent project.

📌 What is MCP?

MCP, full name Model Context Protocol, is an open source protocol launched by Anthropic at the end of 2024, aiming to establish a unified interactive standard between large language models (LLMs) and external data sources and tools. citeturn0news12

🧩 MCP’s realistic analogy: “USB interface” in the AI world

Before USB appeared, the connection between different devices required customized interfaces, which was extremely cumbersome. The emergence of USB has unified the interface standards, making "plug and play" possible.

Likewise, MCP provides similar standardized interfaces for AI systems, allowing different AI applications to be easily connected to a variety of data sources and tools without writing specific integrated code for each combination.

🔍 Why do I need MCP? ——Comparison of traditional architecture vs. MCP architecture

Before we get into the MCP (Model Context Protocol), let’s review the integration of traditional AI applications and the challenges they encounter in actual development.

🧱 Challenges of traditional AI application architecture

In traditional architectures, AI applications usually get data or perform operations by calling the APIs of each service. This method has the following problems:

Complex integration: Each service has a different API interface, authentication method and data format, and developers need to write specific integration code for each service.
Difficulty in maintenance: Updates to services may lead to API changes, and developers need to continuously maintain and update the integrated code.
Missing context: When interacting with multiple services, it is difficult for AI models to maintain a consistent context, affecting the accuracy of responses.
Poor scalability: Every time a new service is added, the integration code needs to be rewrite, which increases the complexity of the system.

🔌 Advantages of MCP architecture

MCP provides a standardized protocol that allows AI applications to interact with various services and data sources in a unified way, bringing the following advantages:

Standardized interface: Integration at one time, available everywhere, significantly reducing development and maintenance costs.
Contextual Preservation: Through a unified protocol, the AI model can maintain a consistent context between different services.
Modular design: Supports unified management of resources, prompt words and tools, and improves the scalability and maintainability of the system.
Dynamic Discovery: The AI model can dynamically discover and use available tools and resources without hard-coded each interface in advance.
Two-way communication: Supports real-time two-way communication. The AI model can not only obtain data, but also actively trigger operations.

📊 Architecture comparison table

characteristic	Traditional architecture	MCP Architecture
Integration method	Each service is integrated separately and requires specific code to be written	Unified protocol, standardized integration
Development complexity	High, need to deal with differences in multiple APIs	Low, unified protocol simplifies development
Maintenance cost	High, service updates may cause integration code to need to be modified frequently	Low, standardized interfaces reduce maintenance
Context Management	Difficult to maintain a consistent context, affecting response accuracy	A unified protocol helps keep context consistent
Extensibility	Bad, every new service needs to be rewrite the integration code	Strong, modular design supports rapid expansion
Dynamic discovery and use	Not supported, each interface needs to be hardcoded in advance	Support, AI models can dynamically discover and use available tools and resources
Communication method	Usually one-way request response mode	Support real-time two-way communication to improve interaction capabilities

Through the above comparison, we can see that MCP architecture is superior to traditional architectures in many aspects, especially in terms of integration efficiency, context management and system scalability. Therefore, adopting the MCP architecture can significantly improve the development efficiency and user experience of AI applications.

⚙️ How MCP works

🧭 Architecture Overview

MCP adopts a client-server architecture, mainly including the following components:

MCP Host: Environments that host AI applications, such as Claude Desktop, IDE, etc.
MCP Client: Runs internally to Host and is responsible for establishing connections with MCP Server.
MCP Server: Provide specific resources, tools and prompt words for use by AI models.
Local data source: Such as file system, database, etc.
Remote Service: Such as GitHub, Google Drive, etc.

🔄 Communication process

initialization: Client sends initialization requests to the Server, negotiates protocol versions and capabilities.
Message exchange: Both parties exchange requests, responses and notifications through the JSON-RPC 2.0 protocol.
Terminate connection:Either party can terminate the connection as needed to ensure the correct release of resources.

🛠️ How to build an agent project using MCP?

🧰 Install MCP SDK

MCP provides SDKs in multiple languages. The following is a case in which TypeScript is used:

npm install @modelcontextprotocol/sdk

🧪 Example: Build an agent that interacts with a PostgreSQL database

Configure MCP Server

{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres", "postgresql://localhost/mydb"]
    }
  }
}

Send a request in the MCP Client

const client = new MCPClient();
await ("localhost");

const response = await ({
  method: "",
  params: {
    tool: "database",
    action: "query",
    args: { sql: "SELECT * FROM users" }
  }
});

();

With the above configuration, the AI model can directly access the PostgreSQL database and perform query operations without writing additional integration code.

🧠 Conclusion

The emergence of MCP has brought unprecedented convenience and flexibility to the development of AI applications. Through standardized protocols, developers can focus more on the implementation of business logic without having to write cumbersome integration code for each data source.

Whether you are a newbie in AI programming or a developer who wants to improve existing systems, MCP is worthy of your in-depth understanding and attempt.

📚 Further reading

Model Context Protocol official documentation
What is MCP? (Model Context Protocol) - A Primer
The Technical Foundation of Next-Generation AI Assistants