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How LLM Choose the Right MCP Tools
In the fast-evolving landscape of artificial intelligence, Large Language Models (LLMs) like ChatGPT and Claude are playing an increasingly pivotal role in automating complex tasks. However, their true potential is unlocked when combined with tool invocation frameworks that allow them to interact with real-world systems. One such powerful framework is the Model Context Protocol (MCP).
This blog post provides a beginner-friendly yet technically rich explanation of how LLMs select the right MCP server and tool(s) to fulfill a given user request. Whether you’re an AI enthusiast, a developer, or someone new to automation, this guide will walk you through the fundamentals of MCP, the role of clients and servers, and how LLMs reason about tool usage.
If you’re new to MCP, I recommend starting with my previous blog MCP Developer Quick Start for a fast and practical introduction.
Step-by-Step Guide: How LLMs Select Tools via MCP
Step 1: Receiving the User Prompt
The process begins when a user interacts with an LLM and inputs a request, such as Diagnose high CPU usage on production server named as prod-server-23.
The LLM processes this natural language input and identifies that this is a task requiring an action beyond simple text generation.
