MCP: The "universal socket" for AI tools, why it will disrupt the technology industry

As AI develops rapidly, we are witnessing the birth of a new standard that may completely change the way AI tools are connected and how they interact with the outside world. This is MCP (Model Context Protocol).

What is MCP?

MCP (Model Context Protocol) is a standardized communication protocol that allows AI tools (such as chatbots, code generators, AI agents, etc.) to seamlessly connect and interact with external systems and tools. Simply put, it provides a framework for AI to "use tools".

Before MCP, every time a developer wanted an AI tool to interact with an external system (like Gmail, Google Drive, or a weather app), they had to write specialized code to establish a connection through an API. This meant that every connection had to be coded up front, and each AI tool had to hard-code its connection method for each external tool individually.

Why is MCP so important?

1. Solved the connection explosion problem

Imagine if there are 1,000 AI tools and 1,000 external tools. In the traditional way, we need to create 1,000×1,000=1,000,000 hard-coded connections. This approach is not only inefficient, but also completely unfeasible at scale.

With MCP, each AI tool only needs to implement the MCP protocol once to connect to all external tools that support MCP. Similarly, each external tool only needs to create an MCP server to be accessed by all AI tools that support MCP.

What is the result?

• Traditional approach: 10,000 AI tools × 10,000 external tools = 100 million connections

• Adopting MCP: 10,000 AI tools + 10,000 external tools = 20,000 implementations

The difference in magnitude is obvious.

2. Improved accessibility and flexibility

Another significant advantage of the MCP protocol is that it can be run in the cloud or on a local computer. This means:

• Enterprises can build local MCP servers to allow AI tools to securely access sensitive data

• Developers can more easily build AI applications that connect to multiple services

• Users can give their AI assistants access to personal tools and data without relying on vendor-specific integrations

3. Promote the development of innovation ecosystem

The existence of a standardized protocol will greatly lower the threshold for innovation. As more and more tools support MCP, we may see:

• More AI tools for professional fields emerge

• Easily add AI capabilities to existing software and services

• Tools created by small developers can quickly integrate with large AI models

How does MCP work?

The working principle of MCP can be simplified into the following steps:

1. Protocol standardization : MCP defines standard communication formats and processes between AI tools and external systems

2. Capability declaration : External tools declare the functions and services they provide through the MCP server

3. Dynamic discovery : AI tools can discover and understand available external tools and their capabilities

4. Seamless call : AI tools can directly call the functions of external tools without pre-coding specific connections

This design makes the whole process very flexible and scalable.

Practical application cases

1. Claude Desktop App + Screenshot Tool

In this case, the Claude desktop application connects to the MCP server at screenshotone.com via MCP. This allows the user to simply provide Claude with a website URL, which Claude can then:

• Request a screenshot service to take a screenshot of this website

• Convert screenshots to HTML

• Analyze content and provide responses

This saves users the trouble of manually taking screenshots and pasting them into the chatbot.

2. Interaction with the local database

Through MCP, the Claude desktop application can connect to a local database. This means that users can:

• "Talk to the database" directly

• Querying information in a database

• Even modify the data in the database through chat

This capability is of great value for data analysis, business intelligence, and rapid prototyping.

3. Enhance AI Integrated Development Environment (IDE)

MCP greatly enhances the capabilities of AI IDE. Developers can:

• Give AI assistants direct access to code bases

• Connecting to a version control system

• Automatically execute and test your code

• Integrate into your build and deployment process

Comparison of MCP with existing technologies

MCP is not the first technology to attempt to solve the AI ​​tool connectivity problem, but it differs from existing solutions in several key ways:

MCP vs Traditional API Integration

• Traditional APIs : Each connection requires custom development, often with hard-coded endpoints, authentication, and data structures

• MCP : Provides a unified interface standard, simplifies the connection process, and supports dynamic discovery and calling

MCP vs platform-specific plugin systems

• Platform plugins : only available for specific AI platforms (such as OpenAI’s plugins), and different versions need to be developed for each platform

• MCP : A platform-independent standard, any AI tool that supports MCP can connect to any MCP server

MCP vs General Automation Platform

• Automation platforms (like Zapier): prefer predefined workflows and triggers

• MCP : Provides a more flexible and direct interface, allowing AI to dynamically select tools based on context and need

Challenges and considerations for implementing MCP

Although MCP offers significant advantages, its implementation faces several challenges:

1. Security considerations

Security becomes even more important when AI tools can access and control external systems:

• Strong authentication and authorization mechanisms are required

• Data protection and privacy controls are critical

• Limiting the scope of AI tools to prevent abuse

2. Standardization and compatibility

As an emerging standard, challenges facing MCP include:

• Ensuring compatibility between different implementations

• Handling version updates and backward compatibility issues

• Balancing strictness and flexibility in standards

3. User experience design

For end users, it can be challenging to understand and control what AI tools do through MCP:

• A clear permission request mechanism is needed

• User-friendly interface shows which external tools the AI ​​is accessing

• Easy ways to revoke permissions or review activity

Future prospects of MCP

As MCP grows and becomes more popular, we may see some exciting trends:

1. Explosive growth of the AI ​​tool ecosystem

When connectivity becomes easy, we may see the emergence of thousands of AI tools designed for specific tasks, each working seamlessly with the others.

2. Enhanced capabilities of personal AI assistants

Personal AI assistants will be able to connect to all of a user’s digital tools and services, from calendars and email to smart home devices and health monitoring systems.

3. Enterprise-level integration

Enterprises might create internal MCP servers that allow AI tools to securely access corporate data and systems while maintaining control over the flow of data.

4. Opportunities to develop new applications

MCPs may enable entirely new types of applications that were previously difficult to implement due to integration barriers.

How do I get started with MCP?

For developers interested in exploring MCP, here are some steps to get started:

1. Understand MCP specifications : Be familiar with MCP protocol specifications and design concepts

2. Participate in the community : Join the MCP-related developer community to learn about the latest developments and best practices

3. Try the sample projects : Get started with open source projects that provide MCP examples

4. Add MCP support to existing tools : Consider adding an MCP server to your existing tools or services

5. Build new applications that leverage MCP : Create AI applications that can dynamically leverage a variety of external tools

in conclusion

MCP (Model Context Protocol) represents a revolution in the way AI tools connect. By standardizing how AI tools interact with external systems, MCP greatly reduces the complexity of integration and paves the way for a whole new generation of intelligent applications.

From 100 million potential connections to just 20,000 implementations, this efficiency gain not only saves development resources, but more importantly, it opens up new avenues for innovation. As more tools and services adopt MCP, we are likely to see an explosion in the variety and functionality of AI applications.

For developers, enterprises, and end users, now is the perfect time to start understanding and exploring MCP. Those who are the first to adopt this standard will be well positioned in a future where AI tools are increasingly connected.

Whether you are building the next generation of AI assistants or adding AI capabilities to existing services, MCP provides a simpler and more powerful way to achieve these goals. This is not just a technological advancement, but a fundamental rethinking of how AI interacts with the world.