What is MCP? Let AI become a true AI Agent, not just a "talking" machine
A major breakthrough in AI assistant technology, a revolutionary shift from passive dialogue to active task execution.
Core content:
1. How MCP technology transforms AI assistants from passive responders to active executors
2. How the MCP protocol breaks through the bottleneck of AI's inability to access external data
3. MCP brings improvements in the practicality and security of AI assistants
The Model Context Protocol (MCP) is an important breakthrough in the development of AI Agent technology. It solves the bottleneck of AI being unable to access external data and enables AI to interact with local computers, databases, and network services through standardized protocols. MCP enables AI assistants to perform more complex tasks, such as database queries, web page debugging, and file management, through the collaborative mechanism of servers, clients, and hosts, thereby transforming AI assistants from passive responders to active task executors.
Without MCP, AI needs to manually upload data or paste it into the GPT dialog box to read and analyze data, and the data that can be analyzed is actually quite limited.
However, the emergence of the MCP protocol allows AI to directly read data, files, and even all data in the computer's cloud drive. The greatest contribution of MCP is that it greatly increases the scope of data that AI can obtain, not just a single file or a small piece of text.
Background of the birth of MCP: Solving the obstacle of AI assistants talking but not doing
Traditional AI assistants (like ChatGPT or Claude) are good at conversation and text generation, but their biggest limitation is that they cannot directly access the user's computer, database or network services. For example, when you ask AI: "Help me organize the data in these Excel files on my computer", it cannot directly read your computer files (otherwise it will have to upload the files to GPT), and can only give you some Excel formula suggestions. There is a considerable isolation in practicality, and this isolation makes AI like an isolated island, unable to communicate with the outside world.
MCP was created to build this "bridge". It is an open source protocol that allows AI assistants to securely access external data, making AI more like a real assistant rather than just a "talking" machine. For example, MCP is like a central controller for a smart home system, allowing your AI assistant to turn on the lights, adjust the temperature, and even check what ingredients are in the refrigerator, rather than just telling you "what functions a certain refrigerator has".
For example, suppose you are an enterprise analyst who wants an AI assistant to help you organize sales data in a SQL database. Without MCP, AI can only tell you how to write SQL query syntax, but with MCP, AI can directly execute SQL queries, pull out data for analysis, and even automatically generate reports. This turns AI from a "suggestor" to a "doer."
Let’s sort out what AI can do now and compare it with the evolution of AI after the advent of MCP:
| instruction | Before MCP | After MCP |
|---|---|---|
| Help me organize sales reports | AI can only say: "Please give me the data and I will help you analyze it." You still have to open Excel and upload it yourself. | AI connects to your filing system through MCP, opens Excel, organizes the data, generates charts, and sends them directly back to you. |
| Help me write the reply and send it to the customer | OK, I have written the content, but I can't send it to the client. Please open Gmail and send it yourself! | AI calls the Email service through MCP, automatically drafts and sends emails, and updates the customer status to "Contacted" in CRM |
| Turn off the lights for me | I can't connect to your light fixtures, but you can probably walk up to the switch yourself and turn the light off. | The AI is connected to the smart home system through the MCP, executes the light-off command, and responds: "The light is off." |
Who developed MCP?
MCP is a protocol released by Anthropic in November 2024. Anthropic is a company focused on artificial intelligence (AI) research and development. Claude AI, a program writer, is Anthropic's representative work.
How does MCP work? Why can AI really “take action”?
MCP is a protocol released by Anthropic in November 2024. Anthropic is a company focused on artificial intelligence (AI) research and development, and the specially programmed Claude AI is Anthropic's representative work.How does MCP work? Why can it allow AI to truly “do things”?
The architecture of MCP consists of several core parts:
MCP Administrator (Host) : Like a command center, responsible for managing the connection between MCP Client and Server. For example, Claude Desktop is an MCP administrator, which allows Client to access your local database and local tools through MCP.
MCP Client : Responsible for the communication between AI and MCP Server, including sending AI commands to MCP Server and receiving messages returned by MCP Server.
MCP Server : Responsible for managing the content commands to be output from the local database (such as adding, modifying, and deleting are all different commands), allowing the Client to select commands to operate. These commands include allowing AI to read databases, access files, and even interact with web pages.
Local Data Sources : Data in your own computer, such as databases, files, software, etc.
Remote Services : Databases on the Internet, such as data in Google Drive and One Drive
The official name "Server" may be misleading. You can imagine that Server is a group of pre-written APIs. After AI comes in through the MCP protocol, there are annotations on the API to help AI understand which APIs it needs to use to complete the task. Therefore, the entire logic of MCP is that engineers first organize the APIs and write annotations, and then let AI take them through the protocol. After taking them, they can analyze the annotations and compare them with the prompt task to execute the API.
MCP allows AI to further develop into "AI Agent"
MCP not only enables AI to read external information, but also turns AI into an intelligent agent (AI Agent) that actively performs tasks. Here are some practical applications:
GitHub Management : The AI assistant reads the commit history of GitHub projects through MCP and even helps you create new repositories, reducing the time for manual operations.
Web page analysis : Through MCP, AI can "check" the HTML structure of a web page, analyze errors, and even help you find out which JavaScript code has a problem.
File processing : AI can directly read local Excel and CSV files, organize data, and even generate reports, instead of just telling you "how to write formulas in Excel."
Imagine you’re a marketer and you want your AI assistant to help you analyze your Google Analytics data to find out which ad campaigns are performing best.
Without MCP, AI can only tell you how to log in to GA and how to view data charts; but with MCP, AI can directly access your GA account, organize data into Excel reports, and even provide optimization suggestions, truly freeing the hands of professionals.
Advantages of MCP: Standardization, Security, and Development Ecosystem
The emergence of MCP brings three major advantages to AI assistants:
Standardized integration : Without MCP, developers must develop integration solutions for different APIs, resulting in high development costs and difficult maintenance. MCP provides a unified standard that allows AI assistants to easily connect to various data sources. Just like USB allows various devices (keyboards, mice, and flash drives) to be connected to computers in a unified manner, MCP also allows AI to communicate with different data systems in a standardized manner.
Data security : All data access in MCP is performed locally by the user and requires explicit authorization to ensure that data is not improperly accessed or leaked. For example, your AI assistant will not access your personal data at will unless you explicitly allow it to read specific files or databases.
Development ecosystem : MCP is an open source protocol, which means more developers can participate and create more powerful tools. Currently, companies such as Anthropic have begun to develop MCP servers, and more third-party developers may join in the future to make AI applications more diverse.
Welcome to a new era of AI agents, MCP will be the next major breakthrough
MCP solves the limitation of AI assistants that they can only talk but not do, making it a truly intelligent agent that can process data, perform work, and even actively manage external systems.Although MCP is still in the development stage and many functions are still being improved, its potential is already very obvious.
In the future, when MCP technology becomes more mature, we may see AI agents helping with more practical tasks, such as:
Automated business reporting : AI automatically pulls data from the financial system to generate monthly revenue reports.
Smart home control : AI directly accesses your smart home system to help you adjust the lights and play music.
Personal AI secretary : AI helps you organize emails, arrange travel, and even handle invoice reimbursement.
The emergence of MCP means that AI assistants are about to enter a new era of personalization and greater functionality.As technology continues to evolve, MCP will become a key bridge for AI to be more deeply integrated into work and life, and it deserves the attention and exploration of all AI developers and users.
