MCP: The Standardization Revolution of AI Agents and Tool Interaction

Introduction

With the rapid development of AI technology, intelligent agents (Agent) are transitioning from theory to practice, becoming the focus of the technology sector. However, how to enable these agents to interact efficiently and safely with the real world has always been a key issue troubling the industry. In November 2024, Anthropic launched the Model Context Protocol (Model Context Protocol, MCP), an open-source standardized protocol, hailed as the "USB-C of AI." MCP promises to revolutionize the development and application model of agents by connecting large language models with external tools and data sources through a unified interface.

For ordinary users, MCP is more like an "AI magic key" that allows non-technical people to easily command intelligent assistants to complete daily tasks. Imagine, just saying "organize my schedule and remind me of tomorrow's meeting," MCP can get it done in seconds; or saying "design a birthday card and send it to my friend," it can instantly generate and deliver it. MCP transforms AI from a complex technology into a handy helper in personal life, saving time, inspiring creativity, and protecting privacy.

This article will comprehensively analyze the full picture of MCP from the dimensions of technical architecture, core advantages, application scenarios, ecological status, potential and challenges, and future trends, providing a detailed guide for technology enthusiasts, developers, business decision-makers, and individual users. Let's explore together how this "key" unlocks the infinite possibilities of AI.

1. What is MCP?

1.1 Definition and Origin

MCP( Model Context Protocol is a standardized protocol released as open source by Anthropic in November 2024, aimed at addressing the fragmentation issue in the interaction between AI models and external tools and data. It is dubbed "the USB-C of AI" or "universal plug," providing a unified interface that allows AI agents to seamlessly access external resources such as databases, file systems, web pages, and APIs without the need to develop complex adaptation code for each tool individually.

The core goal of MCP is to empower AI agents with the ability to transition from "understanding" to "action" through standardization, allowing developers, enterprises, and even non-technical users to customize the agents, becoming a bridge between the virtual intelligence and the physical world. As of March 2025, over 2000 community-developed MCP Servers have gone live, covering scenarios from file management to blockchain analysis, with participation from 300+ GitHub projects and a growth rate of up to 1200%.

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) 1.2 What is MCP for individual users?

For individual users, MCP is the "magic key of AI," making complex intelligent tools accessible. It allows ordinary people to command AI to complete daily tasks through natural language without any programming knowledge, completely breaking down technical barriers. For example, a user can say, "Organize my schedule and remind me of tomorrow's meeting," and MCP will automatically connect the calendar, email, and reminder tools to complete the task in seconds.

MCP is not only suitable for simple tasks but also stimulates creativity and solves life needs.

• Daily Management: Say "List this week's shopping list and remind me", MCP checks the fridge inventory and price comparison websites, generates the list in seconds and sends a text message, saving half an hour.
• Learning and Growth: Students say "Organizing biology notes and creating a review plan", MCP scans notes, connects to the learning platform, outputs review sheets and quiz questions, increasing efficiency by 40%.
• Interest Exploration: Want to learn cooking? Say "Find pasta recipes and ingredients", MC searches the website, checks inventory, generates menus, saving you the hassle of flipping through books.
• Emotional connection: On my birthday, I said "design a card to send to mom", MCP used the design tool to create it and sent it via email, done in 10 minutes.

In terms of privacy, the permission control mechanism of MCP allows users to have complete control over the flow of data. For example, one can set "only allow AI to read the calendar, do not touch the photos", with a reliability of permissions reaching 98%, far exceeding the vague authorization of traditional cloud services.

1.3 Why is MCP needed?

Traditionally, the knowledge of AI models is limited to the training data and cannot access real-time information. For example, if a language model wants to analyze the cryptocurrency market trends in March 2025, it requires manual data input or writing dedicated API calls, which can take hours or even days. More seriously, when multiple models and tools are involved, developers face the "M×N problem"—assuming there are 10 AI models and 10 external tools, 100 custom integrations need to be written, leading to exponential complexity.

The emergence of MCP is precisely to break these barriers. It simplifies the number of connections from N×M to N+M###10 models and 10 tools require only 20 configurations(, allowing AI agents to flexibly call tools like humans through standardized interfaces. For example, querying real-time stock prices and generating reports, which takes 2 hours with traditional methods, only takes 2 minutes with MCP.

2. The Technical Architecture and Internal Operating Principles of MCP

) 2.1 Technical Background and Ecological Positioning

The technical foundation of MCP is JSON-RPC 2.0, which is a lightweight and efficient communication standard that supports real-time bidirectional interaction. It operates through a client-server architecture:

• MCP Host ###: User interaction applications, such as Claude Desktop, Cursor, or Windsurf, are responsible for receiving requests and displaying results.
• MCP Client (: Embedded in the host, establishes a one-to-one connection with the server, handles protocol communication, ensuring isolation and security.
• MCP Server ) Server (: A lightweight program that provides specific functions to connect to local or remote data sources.

Transmission methods include:

• Stdio: Standard Input Output, suitable for local rapid deployment, with latency as low as milliseconds.
• HTTP SSE: Server-Sent Events, supports remote real-time interaction, suitable for distributed scenarios.

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) 2.2 Architecture Design

MCP adopts a client-server architecture, with its core components including:

• Host: User entry point, such as Claude Desktop, responsible for initiating requests and displaying results.
• Client: Communication intermediary, using JSON-RPC 2.0 to interact with the server, managing requests and responses, ensuring isolation.
• Server: Function provider, connects external resources and executes tasks.

( 2.3 primitive function

MCP implements functions through three types of "primitives" ) Primitives ###:

1. 工具###Tools(: Executable functions, AI calls to complete specific tasks.
2. 资源)Resources(: Structured data, used as contextual input.
3. Prompts ): Predefined instruction templates to guide AI in using tools and resources.

In addition, MCP supports the "sampling" ( Sampling ) function, allowing the server to request the language model to process tasks, with users reviewing requests and results to ensure safety and transparency.

( 2.4 Communication Process

The operating mechanism of MCP includes four stages:

1. The user inputs a request.
2. AI analysis request, identify the server to be called.
3. The client connects to the server, and the user approves permissions.
4. Server returns results, AI generates answers.

3. Why should we pay attention to MCP?

) 3.1 Current Pain Points in the AI Ecosystem

• Information Silos: Knowledge is limited to the training data and cannot be updated in real time.
• M×N problem: The integration between multiple models and tools becomes exponentially complex.
• Inefficiency: Traditional methods require embedding vectors or vector searches, which are costly in terms of computation and have long response delays.

( 3.2 MCP's breakthrough advantages

1. Real-time access: AI can query the latest data in seconds.
2. Security and Control: Direct access to data without intermediate storage, with a reliability of 98% in permission management.
3. Low computational load: No need to embed vectors, reducing computational costs by about 70%.
4. Flexibility and Scalability: The number of connections decreased from 100 million ) N×M ### to 20,000 ### N+M ###.
5. Interoperability: One MCP Server can be reused by multiple models.
6. Vendor Flexibility: Switching language models without the need to rebuild infrastructure.
7. Autonomous Agent Support: Supports AI dynamic access tools to perform complex tasks.

( 3.3 Importance and Impact

MCP is not only a technological breakthrough but also a catalyst for ecological transformation. It acts like the Rosetta Stone, unlocking communication between AI and the external world; it is also like the standardization of containers, changing the efficiency of global trade. For example, a pharmaceutical company integrated 10 data sources through MCP, reducing research query time from 2 hours to 10 minutes and improving decision-making efficiency by 90%.

4. Application Scenarios and Case Studies of MCP

) 4.1 Diversified Application Scenarios

1. Development and Productivity:

   • Code Debugging: Cursor AI debugs 100,000 lines of code through Browsertools Server, reducing the error rate by 25%.
   • Document Search: A document server searches 1000 pages of documents in 2 seconds, saving 80% of the time.
   • Task Automation: A certain spreadsheet server automatically updates 500 sales sheets, improving efficiency by 300%.

2. Creativity and Design:

   • 3D Modeling: A certain 3D modeling server reduced the modeling time from 3 hours to 10 minutes, improving efficiency by 18 times.
   • Design Task: A design server assists AI in adjusting layouts, increasing design efficiency by 40%.

3. Data and Communication:

   • Database query: A certain database server queries user records in real-time, response time 0.3 seconds.
   • Team Collaboration: The automated message sending of a certain team collaboration server saves 80% of manual operations.
   • Web Scraping: A web scraping server extracts data from a certain webpage at double the speed.

4. Education and Healthcare:

   • Educational Support: MCP Server connects to the learning platform, AI-generated course outlines, teacher efficiency increased by 40%.
   • Medical Diagnosis: Connect to patient database, AI generates diagnostic reports with an accuracy rate of 85%.

5. Blockchain and Finance:

   • Bitcoin Interactions: MCP Server queries blockchain transactions, enhancing real-time performance to the second level.
   • DeFi Analysis: Analyzing the trading of large holders on a certain trading platform, predicting a profit of $7.88 million, with an accuracy rate of 85%.

4.2 In-depth analysis of specific cases

Taking "File Management" as an example, Claude scans 1,000 files through the MCP Server and generates a 500-word summary in just 0.5 seconds. The traditional method requires manually uploading files to the cloud, taking several minutes. The resource primitives of MCP provide file content, prompt primitives guide the summary, and tool primitives execute operations, working in perfect collaboration.

V. MCP Ecosystem: Current Status and Participants

5.1 Ecological Architecture

The MCP ecosystem has initially taken shape, encompassing four major roles:

1. Client:

   • Mainstream Applications: Claude Desktop, Cursor, Continue.
   • Emerging tools: Windsurf( Education customization), LibreChat### Open source###, Sourcegraph( Code analysis).

2. Server:

   • Database types (500+ including ): Supabase, ClickHouse, Neon, Postgres.
   • Tool category (800+ items ): Resend ( email ), certain payment service ( payment ), Linear ( project management ).
   • Creative category (300+ items ): a certain 3D modeling software (3D), a certain design tool ( design ).
   • Data type: Firecrawl, Tavily( web scraping ), Exa AI.

3. Market:

   • mcp.so: Includes 1584 servers, with over 100,000 monthly active users, providing one-click installation.
   • Other platforms: Mintlify, OpenTools optimize search and discovery.

4. Infrastructure:

   • A cloud service provider: Hosts 20% of the servers, ensuring 99.9% availability.
   • Toolbase: Manage connections, optimize latency by 20%.
   • Smithery: provides dynamic load balancing.

( 5.2 ecological data

• Scale: As of March 2025, the number of MCP Servers will increase from 154 in December 2024 to over 2000, a growth rate of 1200%.
• Community: 300+ GitHub projects participation, 60% of the server comes from developer contributions.
• Activity Level: The Hackathon in early 2025 attracted over 100 developers and produced more than 20 innovative applications, such as shopping assistants and health monitoring tools.

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