### What is MCP? MCP is an open, general, and consensus-based protocol standard led by Anthropic (Claude). It's like installing a "universal interface" for large AI models, enabling AI models to interact seamlessly with different data sources and tools. Similar to the standardized connection method of the USB - C interface, MCP provides a unified approach to connect AI models to various data sources and tools. It aims to replace fragmented Agent code integration, making AI systems more reliable and efficient.  ### MCP Architecture MCP follows the client - server architecture and consists of the following core components: 1. **MCP Hosts**: AI applications that initiate requests, such as chatbots and AI - driven IDEs. 2. **MCP Clients**: Inside the host program, they maintain a 1:1 connection with the MCP server. 3. **MCP Servers**: Provide context, tools, and prompt information for MCP clients. 4. **Local Resources**: Resources in the local computer that the MCP server can access securely, such as files and databases. 5. **Remote Resources**: Remote resources that the MCP server can connect to, such as data provided through APIs.  ### Why Do We Need MCP? Currently, we can't use a single AI application to perform multiple functions such as online searching, sending emails, and publishing blogs simultaneously. It's not difficult to implement these functions individually, but integrating them all into one system is extremely complex. #### Example of Development Scenarios Imagine in daily development, using the AI assistant in the IDE to complete the following tasks: - Ask the AI to query existing data in the local database to assist in development. - Ask the AI to search Github Issues to determine if a problem is a known bug. - Use the AI to send comments on a PR to your colleagues' instant messaging apps (e.g., Slack) for code review. - Use the AI to query and even modify the current AWS or Azure configurations for deployment. With MCP, as long as other services follow the MCP standard, development will become more efficient, just like using a universal interface.  ### Practical Application Cases of MCP #### Example of Programming Assistant Suppose you're using an AI programming assistant (MCP host) to write code. When you need to find the usage of a certain function: 1. The AI assistant sends a request to the MCP server through the MCP client. 2. After receiving the request, the MCP server searches for relevant information from the code repository or documentation. 3. The MCP server returns the result to the AI assistant. 4. The AI assistant generates code or provides an explanation based on the returned information. #### Education Management Scenario After using MCP, you can directly say to the AI: "Help me check the average score of the recent math exam, sort out the list of students who failed the exam into the duty schedule, and remind them to make up the exam in the WeChat group." The AI will automatically: 1. Use MCP to connect to your computer and read the Excel grades. 2. Use MCP to connect to WeChat and find relevant chat records. 3. Use MCP to modify the online document and update the duty schedule. The whole process doesn't require manual operation, and the data won't leave your device, which is both safe and efficient. ### Main Advantages of MCP 1. **Unified Standard**: There's no need to reinvent the wheel. All tools can be connected through a single interface, just like all electrical appliances use the USB - C charging port. 2. **Data Security**: There's no need to upload data to the cloud. The AI processes data directly locally, avoiding data leakage. 3. **Context Understanding**: MCP enables the AI to better understand the context. For example, when receiving a request like "Summarize the key points of last week's class meeting", it can automatically retrieve meeting recordings, chat records, and note documents to provide an answer based on comprehensive information.  By establishing a general standard, service providers can introduce the AI capabilities of their own services based on the protocol, supporting developers to build more powerful AI applications faster. Developers also don't need to reinvent the wheel and can build a powerful AI Agent ecosystem through open - source projects. MCP can maintain context between different applications/services and enhance the overall ability to execute tasks autonomously. ### Summary **MCP is redefining the way AI interacts with the world** — it transforms AI from a "closed thinker" into an "active executor". Whether you're a developer, an enterprise, or an ordinary user, you can unlock a more intelligent and secure automated experience through this protocol. From today on, try to use MCP to make your AI assistant truly "act"!