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In modern AI-driven applications, the Model Context Protocol (MCP) is a powerful way to bridge user queries → intelligent actions.

Here’s how the flow works 👇
1️⃣ User Query – A user sends a natural language request to the MCP Client.
2️⃣ MCP Client → LLM – The client formulates this query into a structured prompt and sends it to the LLM.
3️⃣ LLM Reasoning – The LLM interprets the query and decides which tool / action should be executed (for example: draw a rectangle in Paint, send an email, or query a database).
4️⃣ MCP Server Execution – The MCP Server receives the structured action request and executes the function (like calling APIs, running automation, or fetching data).
5️⃣ Response Back – The result is sent back through the MCP Client → LLM, which formats it into a user-friendly response.

✨ Why it matters?
Decouples reasoning (LLM) from execution (Server)
Makes systems extensible and secure
Allows teams to build agentic AI workflows where natural language drives automation