This week has just reached the middle of the week, and it can be defined as a thrilling week. Since the weekend, the author has been riding a roller coaster in the crypto world every day, and now he is numb. Just after healing the wounds from the tragic PVP of Sol, CZ and Heyi started with a group of KOLs, from "mubarak" to "Mashallah", from the community's spontaneous P-picture competition to He Yi and CZ personally playing memes. This carnival not only created multiple Meme coins with a market value of tens of millions of US dollars, but also made me, a young P-player, add new injuries to my old injuries. If nothing unexpected happens, the Middle East narrative will soon cool down, but the K-lines that will remain on the BNB Chain will not only rise and fall sharply. Those teams that truly understand the needs of the community, such as TUT, which lowers the development threshold through tutorial videos, may be able to reap the final fruits. As the author deepened his research on crpto AI, he found an interesting protocol, MCP, or Model Context Protocol. This open standard developed by Anthropic in December 2024, caught my attention.

What is MCP? How is it related to AI Agent?

MCP (Model Context Protocol) is a new open standard protocol used to establish a secure two-way link between large models and data sources. The core problem it solves is the data acquisition problem (data islands). In the development of AI projects, it can be found that the integration of AI models is complex, and existing frameworks such as LangChain Tools, LlamaIndex, and Vercel AI SDK have problems. LangChain and LlamaIndex have high code abstraction and are too commercialized; Vercel AI SDK is too deeply bound to Nextjs. The goal of MCP is to connect AI assistants with data sources such as content libraries, business tools, and development environments. Its foundation can be traced back to its release in November 2024, and it is designed to help AI models produce more relevant and accurate responses. The technical architecture is shown in the figure below:

Is MCP the "digital nervous system" of AI Agent?

You may ask: "Is it not possible without MCP?" The answer is: Yes, but it is very troublesome. Without MCP, every time the AI agent wants to connect to a new data source (such as a new exchange), the programmer has to write code from scratch, just like every time you want to use a new appliance, you have to solder a plug yourself. With MCP, it is equivalent to equipping the AI agent with a "universal plug" that can be used anywhere, saving time and effort. MCP is like the USB-C unified charging interface, and MCP is unifying the standard for AI to connect everything. Non-technical personnel can also connect smart home devices to AI through MCP (such as controlling smart light bulbs with Slack commands). In addition, a multimodal interface is reserved, and new devices such as AR glasses and brain-computer interfaces can be connected in the future. So MCP itself is just a tool similar to the nervous system, plug and play to read the target data model, and then process and analyze it to finally generate the answer you expect.

Core applications of MCP: Examples

This article will no longer introduce complex terminology concepts, but will use an example close to life to illustrate.

AI Agent: From "Question and Answer" to "Execution"

Scenario 1 : Want to watch a movie on the weekend

- Traditional AI: "What are the nearby cinemas?" (need to enter the location manually)

- MCP+AI: "Open the local map application, search for theaters within 5 kilometers, select IMAX shows, and send the ticket purchase link to WeChat" (fully automatic operation)

Scenario 2 : Stock Investment

• Traditional AI: "Recommend buying XX stock" (may be based on outdated data)

• MCP+AI: Real-time capture of exchange data + macroeconomic indicators + news and public opinion, verification of the calculation process through zero-knowledge proof, generation of encrypted investment reports, and provision of currently available stock trading targets.

MCP's "actual combat" example in the crypto world

Let’s take a look at how MCP and AI agents work in practice in cryptocurrency:

• Help you seize investment opportunities

You ask the AI agent, for example, $AIXBT: "Is it a good deal to buy Ethereum now?" It connects to the exchange through MCP, finds out that the price of Ethereum is rising, then connects to Twitter, sees that everyone is praising it, and immediately replies: "Now is a good time to buy!" This is like when you ask a friend whether he wants to go to a certain restaurant for dinner, he first checks the reviews and then tells you.

• Automatically help you make money

You tell the AI agent: "Buy Bitcoin when it drops to $50,000." It uses MCP to monitor the price of the exchange, and once it drops to $50,000, it immediately places an order to buy. This is like setting an alarm clock to wake you up when the time comes.

• Tell you which coin is the most popular

The AI agent scans social media through MCP and finds that "mubrak" has been mentioned 1 million times today. It can also combine exchange data to tell you that its trading volume has skyrocketed, reminding you: "This coin may take off!" This is like a friend telling you which movie is super popular recently, and you hurry up to buy tickets.

• Keep your wallet safe

MCP also encrypts data when handling private keys or transactions, ensuring that hackers cannot steal your information, just like your bank card number is not leaked when you shop online.

MCP and Encrypted AI Agent

There is no competition between the MCP protocol and the encrypted AI agent. On the contrary, MCP provides the necessary data connection support for the encrypted AI agent, enabling it to complete tasks more efficiently. In the cryptocurrency ecosystem, MCP and the encrypted AI agent are in a cooperative relationship, jointly promoting the application and development of AI technology in this field.

• MCP: Responsible for providing standardized data access interfaces, which is an infrastructure-level protocol.

• Encrypted AI agent: responsible for processing data and performing specific tasks, it is an application-level program.

Looking ahead

MCP has shown great potential in crypto AI agents, connecting AI agents with real-time data to enhance market analysis, automated trading, and secure data processing. Despite the challenges, MCP may be more widely adopted in the future to shape a smarter cryptocurrency ecosystem. For example, existing crypto AI agents, aixbt, kaito, bnkr, etc., can all use MCP as a technology stack, which can greatly reduce development costs and improve product quality. However, it may lose its unique characteristics and features. Some crypto analysts said that Manus + MCP, which became popular last week, was the key to the impact of web3 AI Agent this time. The author has a different view. Simply put, web3 AI Agent is a front-end with AI algorithms, MCP is a transmitter, and the core data and models are the core value. Manus is a large and comprehensive supermarket, which may have everything, and there are many users, but many of them are standard products, which may not meet the needs of thousands of users. The current web3 AI Agent is like a specialty store, which can provide users with more professional and higher value-added products and services. As the author said at the beginning, those teams that truly understand user needs and dig deeper may be able to reap the final fruits.