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MCP Agent : A Practical Guide to Building Context-Aware AI with the Model Context Protocol
Learn the fundamentals of the Model Context Protocol (MCP) and why this open standard is transforming how we connect AI to enterprise data. Through clear explanations and hands-on examples, you’ll discover how MCP serves as the universal “connector” between AI models and the systems where information lives. Context-aware design patterns are illustrated with real-world use cases – from integrating an AI coding assistant with your team’s code repositories and dev tools, to linking a customer support chatbot with knowledge bases and APIs. By replacing ad-hoc integrations with MCP’s standardized approach, you’ll build AI solutions that are more reliable, secure, and scalable.
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.
Inside this practical guide, you'll learn
Master MCP 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 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 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 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 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 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.
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.
Inside this practical guide, you'll learn
Master MCP 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 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 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 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 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 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.
267 pages, Kindle Edition
Published June 8, 2025
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