Are you tired of building chatbots that hallucinate, can't connect to external tools, or don't scale in real workflows? MCP AI Agents, 2nd Edition is the hands-on, code-first guide you've been looking for. Now fully updated for 2025, it teaches AI developers how to build Claude AI agents and OpenAI-driven assistants that truly understand context and act on real-world data. You'll learn to harness the open Model Context Protocol (MCP) - a JSON-RPC 2.0 integration layer - to give your agents tools, memory, and connectivity beyond basic chat. This second edition delivers production-grade AI infrastructure, step-by-step projects, and cutting-edge techniques to create robust context-aware AI systems from scratch.
-
MCP Fundamentals & JSON-RPC 2.0: Master the Model Context Protocol (MCP) and why it's a game-changer for tool-enhanced AI (over 1,000 community-built MCP connectors for Slack, GitHub, databases, etc. exist as of 2025). Learn how to build MCP JSON-RPC servers and securely expose files, APIs, databases, and more to your agents.
-
Claude API Integration & OpenAI Tools: Step-by-step guidance to integrate Claude's API and OpenAI's latest function-calling capabilities into your agents. Work through examples of Claude API integration and GPT-4 tool use, so your AI can search documents, call web services, and execute code seamlessly.
-
Agent Frameworks with Claude/OpenAI: Explore leading agent orchestration frameworks and techniques. You'll connect agent frameworks with Claude/OpenAI models using LangChain and open-source adapters, and compare approaches like OpenAI's functions vs Anthropic's Claude for building flexible agents.
-
Multi-Step Workflows and Tools: Design tool-enhanced workflows that chain LLM reasoning with multi-step tool usage. Build agents that read PDFs, query SQL databases, scrape the web, and summarize content with citations - then output results to reports or emails. Each project is broken down into clear, step-by-step tasks for easy follow-along.
-
LangGraph and CrewAI Orchestration: Orchestrate complex tasks with multi-agent systems. Learn to coordinate LangGraph agents (DAG-based flows) and CrewAI role-based teams, leveraging MCP to share context and tools. The book demonstrates how frameworks like LangGraph and CrewAI can be supercharged with MCP for scalable, collaborative agents.
-
Deployment & Infrastructure: Get practical advice on deploying production-grade AI infrastructure. Containerize your agents with Docker and integrate into CI/CD pipelines. Deploy on cloud platforms (Railway, Render, AWS) with authentication and monitoring, ensuring your AI agents are secure and ready for real-world agent deployments.
