Is AI difficult to implement? MCP may be the key!
How can AI technology overcome the difficulties in implementation? The MCP protocol reveals the answer for you!
Core content:
1. Limitations and challenges of LLM in practical applications
2. Definition, function and working mechanism of the MCP protocol
3. Key components and functions of the MCP architecture
Do you also feel that although the current large language models (LLMs) are smart, they always feel a little lacking when it comes to truly integrating into our daily workflow? They are like knowledgeable "generalists" who have difficulty accessing your private data treasure trove and cannot easily handle those specific operations that require "hands-on".
Don’t worry, maybe we are about to see a breakthrough! Today, let’s talk about an open protocol that has become popular recently - MCP (Model Context Protocol) . This protocol, quietly launched by Anthropic at the end of 2024, is gradually showing great potential to become an industry standard from its initial obscurity.
So, what exactly is MCP? How will it change the way we collaborate with AI? Sit tight, I will show you the core essence of MCP in 3 minutes!
AI is so powerful, why do we still need MCP?
We know that LLMs like GPT-4o and Claude 3.5 Sonnet are full of general knowledge. But they are naturally unable to reach two places:
1. Your exclusive territory, which it cannot enter : For example, the orders in your database, the private thoughts in the note-taking app, or the Excel spreadsheets in your computer, these LLMs are inaccessible by default. 2. It cannot see the outside world : If there is no Internet access, LLM becomes an "information blind box", and it does not know the current stock price, the latest financial report, tomorrow's weather, let alone the cutting-edge technology trends.
Not only that, the "main business" of LLM is to understand and generate text. Letting them directly help you recharge your phone bill, book air tickets, or process images and calculate complex series is somewhat "forcing the issue" and is not their core advantage. At this time, MCP comes on the scene! Simply put, MCP is a set of standardized "communication rules" that defines a set of external tools that allow LLM to "see" information that it originally could not see and "do" things that it originally could not do with the help of these tools. The idea behind this is actually the classic wisdom in the computer field: there is no problem that cannot be solved by adding a layer. If there is, then add another layer! MCP is the layer born to expand the capabilities of LLM.
? How does MCP work? One picture will tell you everything!
To understand how MCP works, we need to recognize three key roles:
1. MCP Server (The Muscle?)
2. MCP Client (The Bridge?)
This is the "bridge" connecting LLM and MCP server, usually embedded in the LLM application. Its responsibilities are simple: receive the "help" signal sent by LLM, accurately forward the request to the appropriate MCP server, and then bring the results processed by the server back to LLM.
3. MCP Host (The Interface?️)
? Three-way collaboration: MCP workflow
The whole process is roughly like this:
(User initiates request -> MCP host -> LLM analysis -> MCP client -> MCP server execution -> returned results -> LLM integration -> MCP host presentation)
✨ Why is MCP a “game changer”?
Over the past year, we have witnessed the rapid evolution of AI models, from GPT-4o to Claude 3.5 Sonnet, and then to DeepSeek R1. The reasoning ability is getting stronger and the hallucination problem is also significantly improved. However, a common feeling is that although there are many AI applications on the market, most of them are new services that "start from scratch", and few can be deeply integrated with our existing workflows and commonly used software systems. In other words, the "connection" between powerful AI models and our existing digital world has been relatively slow.
Imagine, do you want an AI assistant that can help you search for the latest news online, handle emails, and even publish your blog posts with one click? These functions seem easy to use individually, but it has always been a huge challenge to integrate them seamlessly into a unified AI experience.
The emergence of MCP is trying to break down this wall!
The "weaknesses" of LLM mentioned above - charging phone bills, booking air tickets, calling specific APIs, reading local Excel, analyzing Figma design drafts - are gradually becoming a reality through the MCP standard. Don't believe it? You can pay attention to the support of editors such as Cursor or Windsurf for MCP. They can already implement cool operations such as reading Xcode build information through MCP plug-ins. The official also provides some MCP Server examples, demonstrating powerful integration capabilities ( MCP official integration tutorial [1] ):
• Git operations: read, write, and search Git repositories. • GitHub integration: manage repos, operate files, and call GitHub API. • PostgreSQL Queries: Secure read-only database access. • …and more are on the way!
? The current situation of MCP: opportunities and challenges coexist
Because MCP effectively makes up for the shortcomings of LLM, its value is being recognized by more and more people. The community has also become active, and many resource libraries and fan communities have emerged to facilitate everyone to find and share MCP Server implementations:
• awesome-mcp-servers [2] • MCP So [3] • Cline - MCP Marketplace [4] (even set up an MCP "app market"!) But we must also see that MCP is still in its early stages of development and faces some practical challenges:
Security vs. Convenience: A Delicate Balance
One of the core design concepts of MCP is to run the server locally to protect the privacy of user data and prevent sensitive information from being directly transmitted to LLM. This sounds safe. But the problem is that the server running locally may have very large permissions (such as access to the entire file system). Once the server itself has malicious code or vulnerabilities , the risk cannot be underestimated.
For ordinary users, it is almost impossible to determine whether an MCP server is secure. In addition, the current MCP specifications in terms of authentication and permission control are still relatively preliminary and lack mandatory fine-grained management.
The good news is that cloud service providers such as Alibaba Cloud have begun to provide Bailian MCP services [5] , which not only improves ease of use, but also makes it possible to achieve more secure integration in the cloud in the future. However, how to find the best balance between security and convenience, and whether there are enough resources to invest in code review similar to "app stores" will be problems that the MCP ecosystem needs to face and solve.
