Powering up AI models by handing them tools

This tutorial explains how to build a TypeScript application that enables Large Language Models (LLMs) to use tools via the Model Context Protocol (MCP). MCP standardizes how applications provide context to LLMs, allowing them to interact with external tools and data sources. The application acts as a host, connecting the LLM to multiple MCP servers offering various tools, like web searching and file manipulation. Tools are callable functions, while resources provide data without side effects. Prompts act as templates for instructing the LLM. The tutorial uses Anthropic's Claude model and requires setting up an Anthropic API key and installing necessary dependencies. The host connects to specified MCP servers (fetch and filesystem), making their tools available to the LLM. The application then handles LLM requests, executing tools and returning results. The LLM's response is processed, and tool calls are managed through a client-server map. Finally, a sample request demonstrates fetching, summarizing web content, and writing the summary to a file. This enhances the LLM's capabilities beyond simple conversations.