Create a video to explain a mcp server in simple way

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MCP stands for Model Context Protocol. It's a communication protocol that allows AI models to connect with external tools and data sources. Think of it as a bridge connecting two islands - the AI model on one side and external resources on the other. The key problem MCP solves is that AI models need access to real-time data and tools that go beyond their training data. An MCP server is a program that implements the Model Context Protocol to provide specific capabilities to AI models. Think of it like a restaurant: the AI model is the customer who makes requests, the MCP server is the waiter who understands what the customer wants, and the external resources are like the kitchen that actually prepares what's needed. The server acts as an intermediary that speaks both languages - it understands the AI's requests and knows how to fulfill them using external tools and data. Let me show you how MCP servers work step by step. First, the AI model sends a request to the MCP server. Second, the server processes this request and determines what external resources it needs. Third, the server interacts with external resources like databases, APIs, or files to get the required information. Finally, the server formats the results and returns them to the AI model. This creates a seamless flow where the AI can access real-world data through standardized requests. MCP servers provide three main types of capabilities. First, Resources - these give access to file systems, databases, and web content. Think of these as data sources that the AI can read from. Second, Tools - these are active capabilities like calculators, API calls, and system commands that can perform actions. Third, Prompts - these provide templates and instructions that help structure AI interactions. Together, these capabilities make MCP servers incredibly versatile for extending AI functionality. Let me summarize the key benefits of MCP servers. First, extensibility - AI models can access unlimited external capabilities through different servers. Second, security - the server provides controlled access to resources, acting as a protective layer. Third, standardization - one protocol works with multiple AI models and tools, making integration seamless. In summary, MCP servers are intermediaries that safely extend AI capabilities using a standard protocol, enabling AI to interact with the real world beyond their training data. This creates a powerful ecosystem where AI can truly become useful in practical applications.