Maîtrise du Protocole de Contexte Modèle (PCM)

Aperçu

In this very short introductory course, you’ll dive into the Model Context Protocol (MCP)—an open standard designed to transform how AI applications communicate with external tools, services, and data sources. Just as HTTP connected early internet systems and REST revolutionized web services, MCP is redefining the AI landscape by providing a standardized, secure, and scalable framework for integration.

You’ll explore MCP’s modular architecture—hosts, clients, servers—and learn how this design enables real-time, context-aware interactions that empower AI models to deliver richer, more accurate responses. Beyond the technical details, the course examines the new security challenges introduced by MCP, such as context injection, privilege escalation, and tool poisoning attacks.

You’ll discover strategies like policy-bound execution, cryptographic integrity checks, zero trust enforcement, and risk-adaptive controls that help mitigate these risks. By the end of this course, you will be able to:

Explain the Purpose and Evolution of MCP:

Understand the historical context and technological shifts that led to MCP’s development, and how it compares to HTTP and REST.

Describe MCP Architecture and Components:

Identify MCP’s core building blocks and explain their roles in creating seamless, efficient AI integrations. Evaluate Security Implications and Mitigation Strategies:

Assess potential vulnerabilities in MCP deployments and outline best practices to secure AI-powered ecosystems.

Programme

• Guide du débutant sur le MCP
Dans ce court cours, vous explorerez le protocole de contexte de modèle (MCP) révolutionnaire—la nouvelle norme ouverte qui révolutionne la manière dont les applications d'IA se connectent aux outils et aux données. Tout comme le HTTP et le REST ont transformé le web, le MCP ouvre la voie à une intégration d'IA sécurisée, fluide et standardisée à travers des écosystèmes diversifiés. Vous apprendrez comment l'architecture modulaire du MCP—hôtes, clients, serveurs—permet aux modèles d'IA d'avoir un contexte en temps réel, un accès dynamique aux outils, et une interopérabilité améliorée.

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Enseigné par

Fractal Analytics Academy

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Matières

Computer Science