기사 요약
The article demonstrates building a full-stack Python application that leverages local Large Language Models (LLMs) and the Model Context Protocol (MCP).
- It outlines a three-part architecture: a Gradio web UI, a Python backend with a local LLM, and a tool server using MCP.
- The setup enables the local LLM to interact with external tools defined by the MCP specification, such as a file management tool.
- The backend orchestrates requests from the UI, passing them to the local LLM (e.g., using Ollama), which then invokes tools via the MCP server.
- The tutorial emphasizes using MCP as a standard for structured tool invocation, facilitating agentic capabilities in local AI applications.