Recently, the hottest technical concept in the AI circle is undoubtedly MCP (Model Context Protocol).

If you are deeply involved in AI application development and agent building, you must have experienced this: in the past, connecting large models was an extremely tedious task. Connecting to a cloud drive required writing one set of code, adapting to an email service required another interface, local file reading and writing, remote service calls, map tools, database integration — every data source and tool required separate adaptation and repeated debugging.

Scattered adaptation of RAG knowledge bases, non-standard function calling conventions, incompatible interfaces across different large models — all kinds of custom development chaos flourished. Not only were development costs extremely high, but system scalability was also terrible.

On November 25, 2024, Anthropic officially launched the MCP Model Context Protocol, which completely ended this chaos. Hailed by the industry as the universal USB-C interface for the AI field, it relies on neither new models nor new algorithms. With just a set of standardized communication rules, it directly reconstructs the underlying architecture of AI applications, officially pushing AI from the simple chat stage into the new era of Agentic AI.

1. First, Understand: What Exactly Is MCP? (Core Definition)

First, clarify a key understanding: MCP is not a tool, not an SDK, not an API, not a product, but a universal, open communication protocol, belonging to the same category of underlying standards as the familiar HTTP protocol.

Simply put: MCP is the unified language for large models (LLMs) to converse with the outside world.

Large models themselves only possess static pre-trained knowledge and have severe limitations: they cannot read local files, cannot call external tools, cannot obtain real-time data, and cannot link various third-party services. The core mission of MCP is to establish a set of standardized rules so that any large model and any AI client can access various external resources and tools in a unified way, without the need for repeated development of adaptation code.

It specifically serves Context Engineering, providing a standardized communication foundation for the acquisition, transmission, and invocation of AI context, so that the source of context for large models is no longer limited, truly realizing "everything can be connected to AI."

2. MCP Core Architecture: Three Components to Understand the Complete Operational Logic

MCP adopts a classic client-server architecture. The entire system consists of three core parts: MCP Server, MCP Client, and MCP Host. The three have clear division of labor and work together to form a complete closed loop for AI's external interaction.

1. MCP Server (Server-side: Provider of Resources and Tools)

The MCP Server is the "resource warehouse" and "capability base" of the entire system. Its core role is to provide various usable contexts and tool capabilities for large models. It defines standardized interaction rules, clarifies the communication method between the server and client, and uniformly packages various scattered external capabilities for large models to call.

All external resources and tool services that need to be called by AI can be packaged as MCP Servers, mainly falling into two categories:

• Resource-type Servers: Local files, remote file servers, various databases, cloud drives, SaaS services like Feishu/DingTalk, Amap, Gmail email services, etc., providing data context for AI;
• Tool-type Servers: Calendar creation, email sending, terminal command execution, remote device control, data processing scripts, etc., providing executable action capabilities for AI.

2. MCP Client (Client-side: Standardized Connector)

The MCP Client is a "standardized plugin interface" installed on the AI side, equivalent to the USB-C port. Mainstream AI editors and agent tools have already adapted MCP clients, including Cursor, Trae, Claude Code, OpenAI Codex, etc.

Developers only need to do simple configuration on the client side to associate various MCP Server services with one click, just like installing plugins, without writing complex integration code. All clients follow the same set of MCP protocol standards, perfectly adapting to all compliant servers, truly achieving configure once, use everywhere.

3. MCP Host (Host: The Main Body of the AI Agent)

The MCP Host is the actual AI Agent itself, i.e., agent applications like Claude Code. Its core working logic is highly disruptive: it does not rely on pre-trained knowledge but actively retrieves available client resources through real-time reasoning.

When a user issues a task, the AI host automatically scans the bound MCP clients, identifies the currently callable files, tools, and data services, retrieves real-time context to complete the task, completely breaking free from the limitations of the model's static knowledge, giving AI the agent capabilities of real-time perception, real-time operation, and autonomous execution.

3. Why Can MCP Disrupt the AI Industry? Ending the Chaos of Traditional Development

Before the birth of MCP, AI application development long faced the M×N integration dilemma, which was also the biggest pain point for implementing RAG and function calling:

There are dozens of large models and hundreds of external tools and data sources on the market. For each model to connect to each service, a separate adaptation interface needed to be developed. Model updates and service iterations required re-debugging, leading to extremely high code redundancy and explosive maintenance costs. Moreover, each system was isolated from the others, forming serious data and capability silos.

The emergence of MCP directly eliminates all adaptation differences, bringing three revolutionary values:

1. Standardized Unification, Ending Scattered Adaptation

One protocol adapts to all models and all services. Whether it's Claude, OpenAI, or other mainstream large models, whether it's files, emails, maps, or database services, they can all be freely interconnected through the MCP standard, completely bidding farewell to the development model of reinventing the wheel.

2. Greatly Expanding the Boundaries of AI Context

Traditional AI's knowledge is limited to training data and fixed knowledge bases, but MCP allows AI to read local/remote files in real-time, fetch third-party real-time data, and execute various tool operations. The sources of context are infinitely expanded, making answers and execution capabilities more accurate, more real-time, and more practical.

3. Pushing AI from Chatbot to Agentic AI

This is the most core industry transformation brought by MCP. In the past, AI was just a "conversational robot" that could only passively answer user questions; but AI based on the MCP protocol can autonomously retrieve resources, autonomously call tools, and autonomously complete complex tasks, possessing the agent characteristics of autonomous decision-making and autonomous execution, truly stepping into the Agent era.

4. Hands-on: Local File System MCP Deployment (Including npx Practical Details)

The official provides an out-of-the-box server-filesystem file system MCP Server. Its core role is permission isolation, only allowing the large model to read and write locally specified folders, ensuring the security of local file access. The entire local service is based on stdio standard input/output communication, and with the Node ecosystem's npx tool, the service can be quickly started. Two deployment methods are explained below.

Two Ways to Start the Service

Method 1: Global Installation of the Service Package

Install the file system service globally on the local machine, so it can be called directly from any subsequent terminal:

bash

npm i -g @modelcontextprotocol/server-filesystem

Method 2: npx Direct Run Without Installation (Recommended)

No need to download dependencies globally in advance. npx will temporarily pull the corresponding package from the npm repository and start the process instantly; the -y parameter automatically agrees to dependency installation confirmation pop-ups, requiring no manual interactive input:

bash

npx -y @modelcontextprotocol/server-filesystem your_project_directory_path

Detailed Explanation of Standard Client Configuration Example

Write JSON into the MCP configuration file of tools like Cursor, Claude Desktop, etc. The complete fields correspond to mcpServers, stdio, and the file service package, explained layer by layer:

json

{
  "mcpServers": {
    "local-file": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "\Users\ZhuYi\OneDrive\Desktop\workspace\ZhuYi_ai\ai\mcp"
      ]
    }
  }
}

Field Breakdown:

1. mcpServers: The root node of the configuration, managing all MCP services uniformly, supporting the simultaneous configuration of multiple independent Servers like files, maps, and mailboxes;

2. type: stdio: The mainstream communication mode for local MCP, completing data interaction between Client and Server through process standard input/output, requiring no extra ports or network services, suitable for local development; cloud-based remote MCP services generally use SSE mode;

3. command: npx: Specifies the execution program to start the service, relying on npx to achieve fast startup of MCP services without global dependencies;

4. args parameter analysis:

   • -y: npx automatically confirms installation;
   • @modelcontextprotocol/server-filesystem: The official file system-specific MCP Server package, specifically responsible for local file resource reading, modification, and querying;
   • The path at the end: Limits the directory accessible by AI, playing a security isolation role.

After configuration, restart the client. The AI can only read and write files within the set directory, automatically reading the project's local context, saving complex interface development.

5. Summary

If large model pre-training is the brain of AI, MCP is the standardized channel connecting to the real world. It is not a partial functional optimization, but a reconstruction of the overall architecture of AI applications.

The entire local implementation chain relies on three core elements: mcpServers for unified management of all external services, stdio for lightweight local communication, and server-filesystem for standardized local file resource services, coupled with the extremely simple deployment capability of npx, allowing developers to connect various external data and tools at extremely low cost.

In the future, all kinds of AI editors, SaaS services, and databases will adapt to the MCP standard, breaking down tool and data silos, becoming an indispensable underlying communication specification for the Agent era.