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MCP Agent: A Practical Guide to Building Context-Aware AI with the Model Context Protocol

MCP Agent: A Practical Guide to Building Context-Aware AI with the Model Context Protocol

          
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About the Book

Moving from theory to practice, MCP Agent delves into advanced techniques that every experienced developer will appreciate. You'll explore Graph RAG (Graph-based Retrieval Augmented Generation) workflows to enrich your agents' knowledge using connected knowledge graphs alongside vector databases. You'll also master LangGraph - LangChain's powerful new framework for graph-driven agent orchestration - to create complex LLM workflows with fine-grained control. Learn how to structure your AI agents as deterministic graphs or state machines, enabling transparent decision flows, tool use, retries, and conditional logic for even the most challenging tasks. Throughout the book, Zussman emphasizes a hands-on, modular design approach. Each chapter is packed with code snippets, step-by-step tutorials, and real-world examples that bring concepts to life. You'll build agents that connect to popular tools and data sources (like databases, document repositories, web services, and dev ops tools) via MCP, equipping your AI with relevant context on the fly. Discover how to implement robust agent memory and state management techniques so your agents remember important information across interactions. You'll also learn best practices for evaluating an agent's reasoning and responses, with guidance on testing and debugging to refine your agents' performance before deployment. Inside this practical guide, you'll learn to: Master MCP Fundamentals: Understand the Model Context Protocol architecture and how to use this open standard to give your AI secure access to databases, knowledge bases, and APIs for rich context integration. Integrate Tools and Data Sources: Connect your agents to the outside world through tool integration and API calling, so they can retrieve up-to-the-minute information (files, code, queries) and interact with services in real time. Build with LangGraph Workflows: Leverage the LangGraph framework to orchestrate complex agent behaviors. Design graph-based workflows that chain LLM prompts, tool uses, and decisions with full control over loops, branching, and error handling. Implement Memory & Context Management: Incorporate long-term memory stores and context summarization to maintain conversational history and user preferences. Use Retrieval Augmented Generation (RAG) and Graph RAG techniques to inject relevant knowledge graph data into your agent's responses for greater accuracy. Evaluate and Refine Agents: Employ proven strategies to test your AI agents, from unit-testing tool functions to benchmarking output quality. Learn how to debug reasoning steps, handle edge cases, and iteratively improve your agents' decision-making. Deploy Scalable Intelligent Agents: Get best practices for deploying your AI agents in production. Containerize and integrate agents into applications, monitor their performance, and scale up securely - whether you're building an internal enterprise assistant or a customer-facing AI service. Written for software engineers and AI professionals, MCP Agent balances deep technical insights with a practical, engineering-focused tone. You'll gain the know-how to build scalable, intelligent agents that are grounded in real-world context and robust design principles. Whether you're architecting a new enterprise AI assistant or enhancing an existing LLM application, this book provides the patterns and tools to succeed. Take your AI agent development to the next level with MCP Agent - and create the context-aware, tool-using, autonomous agents that are defining the future of intelligent software.


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Product Details
  • ISBN-13: 9798285374435
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 226
  • Spine Width: 12 mm
  • Weight: 399 gr
  • ISBN-10: 8285374432
  • Publisher Date: 26 May 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: A Practical Guide to Building Context-Aware AI with the Model Context Protocol
  • Width: 178 mm


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