Introducción al Protocolo de Contexto del Modelo (MCP)

Resumen

Integrate AI/LLM applications with APIs, databases, and filesystems easier than ever before with the Model Context Protocol (MCP). Why Learn the Model Context Protocol?Large language models can't access real-time data or take actions on their own, and wiring each tool or API with custom code doesn't scale.

The Model Context Protocol (MCP) solves this with a single, standardized way for AI applications to connect to external tools, data, and services—often called "the USB-C port for AI." In this course, you'll build MCP servers and clients from scratch in Python and wire them to an LLM.How Do I Build and Connect My First MCP Server?You'll start by learning the MCP architecture—host, client, and server—and the three primitives every server exposes:

tools, resources, and prompts. Then you'll build a currency converter server using FastMCP, add docstrings and type hints so an LLM can discover your tools, and write an async Python client that lists and calls those tools over stdio transport.How Do I Give an LLM Real-Time Tools and Context?Tools alone aren't enough—models also need data and behavioral instructions.

You'll add resources for read-only context and prompts to guide the model when inputs are vague, then wire all three primitives into an OpenAI LLM using the five-step tool-calling workflow so it can answer confidently or ask for clarification when it should.How Do I Take MCP Servers to Production?Real-world servers need more than happy-path code. You'll swap file-based resources for database-backed queries, add request timeouts, structured error handling, and secure API authentication that keeps keys server-side.

Finally, you'll connect to a third-party MCP server and see that the same client code works with any server that speaks the protocol.

Programa

• Los bloques de construcción de MCP
Descubre cómo MCP puede facilitar la integración de aplicaciones de IA con sistemas circundantes como nunca antes. Aprende cómo funciona MCP, cómo definir tus propias herramientas MCP, y cómo construir el puente entre cliente y servidor.
• Aplicaciones de LLM habilitadas por MCP
Añade recursos y avisos a servidores MCP para exponer a LLMs a un contexto de solo lectura e instrucciones clave para alterar su comportamiento. Experimenta con LLMs y conéctalos a tu servidor MCP para que pueda llamar herramientas y recuperar contexto.
• Preparación de servidores MCP para producción
Descubre qué se necesita para llevar a los servidores MCP a producción sumergiéndote en bases de datos y APIs en los servidores MCP, y las consideraciones adicionales que aportan. Finalmente, integra MCPs de terceros de manera segura y confiable, para que no tengas que reinventar la rueda para tus integraciones favoritas.

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Impartido por

James Chapman and Korey Stegared-Pace

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Materias

Artificial Intelligence