MCP Detailed Explanation丨Key Technologies Behind Agent Outbreak
Unveiling the technical veil behind Agent products, how the MCP protocol reshapes the interaction between AI models and data.
Core content:
1. MCP definition: the role of the model context protocol and its unified standards
2. MCP practical application: simplifying the connection process between AI models and external tools/data
3. MCP future value: promoting the development of AI technology and realizing efficient "Internet of Everything"
Last week, the Chinese team released the first universal agent product Manus. It is no exaggeration to say that it has brought the concept of Agent to a new level, causing many friends who do not have a deep understanding of AI technology to flock to many "Manus exchange groups" for fear of missing out.
However, many friends have not received the Manus invitation code yet, and I, a small transparent person, am no exception. Some of them can only provide cases and ask friends with invitation codes to help test them. I haven’t tested it myself, so I won’t make too many comments for now.
However, it made MCP popular. Putting aside Manus's own reputation, I think it is a good thing. MCP came out in November last year. After communicating with many technical experts around me, we all felt that the value of MCP was far underestimated. Many people did not think so. At that time, I also made a simple analysis in an article.
Many non-technical group members also said that they couldn’t understand it. Today, following Manus’s advice, I will try to use the simplest language to explain to you what MCP is, what it has, and what value it will have in the future.
01 What is MCP
MCP is a model context protocol first proposed by Claude. Its advantage lies in unified standards, which enables two-way communication between AI models/assistants/agents and other applications and external tools/Internet data/local data.
The picture below can help you understand. You can think of MCP as the "USB-C port" in the AI field. Just like the USB-C port makes it easier for your computer to connect to various devices, MCP allows different AI models to easily connect to external tools and data sources, making it easier for AI models to obtain data, tools and services.
Image credit: Norah Sakal
MCP is not that mysterious. It just serves as a unified standard, that is, to build a bridge between various AI models, intelligent agents and other clients and some external tools, data, and services, so that everyone can visit each other through this bridge (MCP) and even do business on it.
02 What is the use of MCP
In the past, if we wanted to use the capabilities or services of external tools in our own applications, including many AI models and intelligent agents, we had to call APIs. If we wanted our applications to use our own local data, we had to copy and paste or upload and download, which was very troublesome.
Before MCP, AI systems had to write code and call APIs to interact with external tools/services. This meant that each specific connection had to be manually programmed in advance, and each API had independent code, documentation, authentication methods, error handling, and subsequent maintenance, which was extremely inefficient and time-consuming.
If multiple AI systems are connected to multiple external tools, each AI system and external tool must be configured separately.
For example:
Theoretically, if there are 100 AI systems and 100 external tools, 100×100 = 10,000 independent connection codes need to be written. If, as predicted by everyone, there will be tens of billions or hundreds of billions of agents and silicon-based robots (which can be regarded as an AI system) in the future, then for such a large group to realize the "Internet of Everything", the workload will be astronomical.
To use an analogy, APIs are like various doors, each with its own unique key and usage rules.
Image credit: Norah Sakal
With MCP, the tedious work of one-to-one development and use of the above API can be solved, because the MCP protocol unifies all the above problems. Everyone only needs to comply with the MCP protocol and expose the tools and data through the MCP server to achieve data interoperability and tool interoperability.
Key benefits of MCP:
Simplified development process: You only need to write once to achieve multiple integrations. When faced with new integration requirements, there is no need to rewrite custom code, which greatly improves development efficiency.
Extremely flexible: When you need to switch AI models or tools, there is no need for complex reconfiguration. It can easily adapt to different technology options, providing users with a convenient user experience.
Real-time and rapid response: The MCP connection remains active at all times, updating contextual information in real time and supporting instant interaction, ensuring that users receive timely and accurate feedback during use.
Security and compliance are guaranteed: A comprehensive access control mechanism is built in, and standardized security practices are followed to ensure the security and compliance of the system at all levels, giving users peace of mind.
Powerful scalability: As the AI ecosystem continues to grow and develop, new features can be easily added by simply connecting to new MCP servers, seamlessly connecting to the ecosystem's expansion needs, and continuously providing users with more innovative services.
It is more intuitive to compare directly by looking at the pictures.
03 The future value of MCP
The main purpose of the MCP protocol is to unify standards and provide a more convenient and efficient way to achieve a digital world where everything is connected.
Baidu CEO Robin Li once said that 2025 would be the first year of the explosion of intelligent agents. At that time, the explosion of intelligent agents seemed to have reached an industry consensus. With the explosive popularity of the Agent product Manus, the first shot of Agent in 2025 was fired. Next, I believe that more and more Agent products will emerge in the market. Many people even predict that the next popular Agent product will be Coding Agent.
However, the three core capabilities of Agent intelligence are: planning (to do list), task execution, and memory.
Among them, the core capability of executing tasks is mainly through the call of external tools, which is the key to making the Agent intelligent body "move" and truly realize the interaction between the Agent intelligent body and the real world.
GeekSavvy has also built an AgentX agent knowledge base, which is free and open for everyone to learn, so that non-technical novices can also build their first agent. At the same time, we also welcome friends to join in the construction, making the AgentX agent community more open and rich.
AgentX official website:
https://agentx.fan/
AgentX Agent Knowledge Base:
https://hyperspace.feishu.cn/wiki/HYchwsba5iBBg7k8OdAc8MgFnvH?from=from_copylink
For example:
The most powerful open source OWL currently available, when searching for information about movies currently showing in London, the Agent intelligent body autonomously calls the Chrome search tool to obtain and feedback real-time information about the theater with extremely high accuracy.
In the future, tens of millions of Agents will enter our lives. The execution capability of the Agent is an important sign of the evolution of AI ChatBot to Agent, which is what we call autonomous execution. MCP is undoubtedly a boundary channel that gives Agents a variety of capabilities.
Consider the following three scenarios:
1. Travel Planning Assistant
When using the API: writing code for Google Calendar, email, and flight booking is tedious and complicated.
When using MCP: The AI assistant directly uses the MCP unified protocol to check calendars, book flights, and send email confirmations, without having to integrate each tool separately.
2. Smart IDE (Code Editor)
When using the API: Manually connecting to the file system, version management, package management, and documentation is time-consuming and laborious.
When using MCP: The IDE connects all functions at once through MCP, bringing richer contextual support and more powerful intelligent suggestions.
3. Complex data analysis
When using an API: Manually manage connections to each database and data visualization tool.
When using MCP: AI automatically discovers and connects multiple databases and visualization tools, making it easy to complete analysis tasks through a unified MCP interface.
Last but not least
Of course, MCP was proposed by Claude Company. The idea of unifying standards is very good, but it is difficult to expect the whole world to do things according to your standards. It requires huge financial support and a more open mind for everyone to build together, so that this ideal can be realized.
In the future, everyone will definitely want others to use their own standards, and it will just depend on who can come out on top.
Finally, I would like to end with a quote from Lei Jun: "Technology is not about being high above, but about serving the people."
