AI Agents with MCP : A Hands-On Guide to Building Context-Aware Agents with Claude, OpenAI, and LangChain

Build the Next Generation of AI Agents with MCP
The future of AI isn’t just about smarter models—it’s about smarter systems. With the Model Context Protocol (MCP), developers can finally connect large language models to tools, workflows, and real-time context in a standardized, reliable way. AI Agents with MCP is your hands-on guide to mastering this emerging ecosystem.
Whether you’re working with Claude, OpenAI, or LangChain, this book shows you how to design and deploy context-aware agents that don’t just answer questions but take meaningful action. From your first “Hello MCP” call to building multi-agent collaboration systems, you’ll gain the practical skills to bring cutting-edge AI workflows into production.
Inside, you’ll learn how to:

• Understand MCP fundamentals — JSON-RPC, servers, clients, and context sharing.
• Build and expose MCP servers that wrap tools like file systems, vector databases, and APIs.
• Connect Claude, OpenAI, and LangChain agents to MCP endpoints for seamless integration.
• Implement Retrieval-Augmented Generation (RAG) with vector stores and document Q&A agents.
• Design workflow automation agents that combine email, file management, and task pipelines.
• Coordinate multi-agent systems with Researcher–Critic patterns and role-based designs.
• Deploy and scale MCP agents using Docker, Kubernetes, and cloud platforms (AWS, GCP, Azure).
• Secure your agents with authentication, access control, and best practices for compliance.
• Debug and optimize common pitfalls: JSON-RPC errors, latency, scaling bottlenecks, and context overflows.

💡 Hands-On Projects You’ll Build

• A file-system MCP server and client
• A LangChain-powered Document Q&A agent with Claude
• An email + file management automation agent
• A personal knowledge base assistant
• A two-agent collaboration system (Researcher + Critic)

By the end of this book, you’ll be able to design, debug, and deploy MCP-based AI agents that are production-ready, context-aware, and interoperable across frameworks.