Is your definitive guide to building robust, context‐aware AI agents that scale from proof-of-concept to production. Rather than scatter state-management logic across bespoke services, this hands-on manual teaches you how to harness a simple JSON-RPC interface—context.save, context.get, context.prune, and tool.invoke—to centralize conversational memory, orchestrate external tools, and maintain session integrity across distributed clusters.

Inside these pages, you’ll find real-world scenarios drawn from official reference implementations: from booking flights and triaging support tickets to streaming patient symptoms and firing sub-100 ms trading alerts. Each chapter walks you through theories, clear ASCII diagrams, and fully runnable Python code, then challenges you with mini-projects, troubleshooting deep dives, and end-of-chapter exercises. You’ll explore everything from JSON-RPC foundations and sliding-window retention policies to modular plugin architectures, transport layers (WebSockets, gRPC, message queues), observability with structured logging and OpenTelemetry tracing, and scalable deployments on Docker, Kubernetes, serverless platforms, and edge gateways.

Whether you’re an AI engineer, backend developer, or architect, you’ll come away with a battle-tested toolkit for creating next-generation agents that preserve context, handle errors gracefully, and evolve through governed extensions. By the time you finish, you’ll not only speak MCP fluently but also be prepared to contribute to open standards, build dynamic agent pipelines, and pioneer tomorrow’s adaptive, self-configuring workflows.

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