Model Context Protocol (MCP) in AI Agents: The Ultimate Guide to Building Context-Aware, Integrated, and Intelligent AI Systems
暫譯: AI代理中的模型上下文協議(MCP):構建具上下文感知、整合及智能的AI系統的終極指南

Devline, Morgan

  • 出版商: Independently Published
  • 出版日期: 2025-04-08
  • 售價: $780
  • 貴賓價: 9.5$741
  • 語言: 英文
  • 頁數: 280
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9798317153311
  • ISBN-13: 9798317153311
  • 相關分類: 人工智慧
  • 海外代購書籍(需單獨結帳)

商品描述

For developers, software engineers, technical architects, and AI professionals alike, Model Context Protocol (MCP) in AI Agents: The Ultimate Guide to Building Context-Aware, Integrated, and Intelligent AI Systems is the definitive resource for creating AI that truly understands its environment. This comprehensive guide spans from foundational concepts to advanced techniques, ensuring you can bridge fragmented AI integrations, infuse real-time context, and achieve unprecedented scalability in your projects. It's like giving your AI a universal adapter (the "USB-C of AI") to plug into any data source or tool - finally, your AI systems can stay connected and relevant at all times.

Solve Key AI Integration Challenges

MCP directly addresses the biggest pain points in modern AI development. In this book, you'll learn how MCP helps you overcome:

  • Fragmented Integrations: Tired of building one-off connectors for every data source or API? See how MCP replaces ad-hoc integrations with a single unified protocol, so your AI agents can interface with any tool or database through one standard approach.
  • Lack of Context: Ever get irrelevant answers because your AI is stuck in a silo? MCP enables real-time context streaming, feeding your AI assistants live data and situational context. The result: responses that are always up-to-date, relevant, and tailored to your needs.
  • Poor Scalability: Worried that adding new features will break your system? MCP's scalable architecture lets you plug in new data sources and capabilities on the fly. Build future-proof AI systems that grow without the usual integration headaches.

What You'll Gain

By diving into this guide, you will acquire practical skills and insights to take your AI systems to the next level:

  • Master Context-Aware AI: Learn step-by-step how to implement context-aware AI agents using the MCP standard, so your applications can seamlessly tap into external knowledge and data.
  • Seamless Integration Skills: Gain hands-on knowledge to connect language models with files, databases, APIs, and enterprise tools via MCP, replacing brittle pipelines with robust, standardized connections.
  • Scalable AI Design Patterns: Discover proven design patterns for architecting AI systems that scale. You'll learn how to design modular AI agents that can handle growing workloads and integrate new data sources with minimal effort.
  • Real-World Implementation Experience: Through tutorials and case studies, build the confidence to apply MCP in production. Develop secure and reliable AI solutions using best practices gleaned from real projects.

Inside This Ultimate Guide

Morgan Devline unpacks the MCP architecture and its ecosystem in depth, equipping you with both the "why" and the "how" of context-driven AI. Key topics include:

  • MCP Architecture & Fundamentals: Understand the core MCP framework - the servers, clients, and protocols that make up this open standard - and how to leverage them to design context-aware systems from the ground up.
  • Real-Time Context Streaming: Implement techniques for streaming live context into your AI agents. Learn how to continuously feed data (documents, events, sensor info, etc.) to your models so they can react and adapt in real time.
  • Security & Privacy: Build secure AI integrations with confidence. Explore how MCP handles permissions, sandboxing, and data privacy, and learn best practices to protect sensitive information in context-aware AI workflows.
  • MCP SDKs & Tools: Get up to speed with the official MCP SDKs and libraries.
  • Case Studies & Real-World Projects: Go beyond theory with detailed case studies and project walk-throughs.
  • Hands-On Exercises: Each chapter includes practical exercises and mini-projects to solidify your understanding.

商品描述(中文翻譯)

對於開發人員、軟體工程師、技術架構師和人工智慧專業人士來說,《AI Agents中的模型上下文協議(MCP):構建具上下文感知、整合及智能的AI系統的終極指南》是創建真正理解其環境的AI的權威資源。這本全面的指南涵蓋了從基礎概念到進階技術,確保您能夠彌合分散的AI整合,注入即時上下文,並在您的專案中實現前所未有的可擴展性。這就像給您的AI一個通用適配器(AI的「USB-C」),可以連接到任何數據來源或工具——最終,您的AI系統可以隨時保持連接和相關性。

解決關鍵的AI整合挑戰

MCP直接針對現代AI開發中的最大痛點。在這本書中,您將學習MCP如何幫助您克服:
- 分散的整合:厭倦了為每個數據來源或API構建一次性連接器?看看MCP如何用單一統一協議取代臨時整合,讓您的AI代理可以通過一種標準方法與任何工具或數據庫進行接口。
- 缺乏上下文:是否曾因為您的AI被困在孤島中而得到不相關的答案?MCP使即時上下文流媒體成為可能,為您的AI助手提供實時數據和情境上下文。結果是:回應始終是最新的、相關的,並且符合您的需求。
- 可擴展性差:擔心添加新功能會破壞系統?MCP的可擴展架構讓您可以隨時插入新的數據來源和功能。構建未來可持續的AI系統,無需擔心常見的整合麻煩。

您將獲得的收穫

通過深入這本指南,您將獲得實用的技能和見解,將您的AI系統提升到新的水平:
- 精通上下文感知AI:逐步學習如何使用MCP標準實現上下文感知的AI代理,讓您的應用程序能夠無縫接入外部知識和數據。
- 無縫整合技能:獲得實踐知識,通過MCP將語言模型與文件、數據庫、API和企業工具連接,取代脆弱的管道,實現穩健的標準化連接。
- 可擴展的AI設計模式:發現經過驗證的設計模式,用於架構可擴展的AI系統。您將學習如何設計模組化的AI代理,以處理不斷增長的工作負載,並以最小的努力整合新的數據來源。
- 實際實施經驗:通過教程和案例研究,建立在生產環境中應用MCP的信心。使用從實際專案中獲得的最佳實踐開發安全可靠的AI解決方案。

這本終極指南的內容

Morgan Devline深入解析MCP架構及其生態系統,為您提供上下文驅動AI的「為什麼」和「如何」。主要主題包括:
- MCP架構與基礎:了解核心MCP框架——構成這一開放標準的伺服器、客戶端和協議,以及如何利用它們從零開始設計上下文感知系統。
- 即時上下文流媒體:實施將實時上下文流入您的AI代理的技術。學習如何持續向您的模型提供數據(文件、事件、傳感器信息等),使其能夠實時反應和適應。
- 安全與隱私:自信地構建安全的AI整合。探索MCP如何處理權限、沙盒和數據隱私,並學習保護上下文感知AI工作流程中敏感信息的最佳實踐。
- MCP SDK和工具:快速了解官方MCP SDK和庫。
- 案例研究與實際專案:通過詳細的案例研究和專案步驟超越理論。
- 實踐練習:每章包括實用練習和迷你專案,以鞏固您的理解。