This podcast episode explores the concept of agentic AI and how to make language models useful in real-world applications. The discussion centers around the Model Context Protocol (MCP) as a solution for streamlining the integration of tools with AI agents. They define different types of tools—function tools, built-in tools, and agent tools—and delve into best practices for tool development, emphasizing documentation, task-focused design, concise output, and error handling. The podcast also covers the architecture of MCP, its components (host, client, server), communication protocols, and the importance of JSON schemas for tool definition. Finally, the speakers address the challenges of context window bloat and security, proposing strategies like tool retrieval and wrapping MCP with enterprise-grade security measures.
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