Hands-On With Anthropic's Model Context Protocol — Build Production AI

Reading the official MCP docs tells you what the protocol supports. This hands-on workshop shows you how to make decisions when building with it: which tool schemas work well for LLM orchestration versus which cause agent confusion, how to structure error responses for effective recovery, when to use tools versus resources versus prompt templates, how to design context boundaries that prevent pollution between agents, and how to integrate MCP with the broader context engineering stack including RAG and semantic blueprints. These are judgment calls the docs cannot teach.

This hands-on workshop covers the core Anthropic MCP features: tool definitions with typed JSON Schema inputs and outputs, resource definitions for shared knowledge access, prompt templates for structured agent instructions, the server lifecycle including initialization and capability negotiation, error types and structured failure handling, the sampling interface for LLM invocations through MCP, and the roots feature for filesystem access in agent environments.

Anthropic's Claude models are designed to work natively with MCP. Claude understands MCP tool descriptions and can invoke tools through the protocol when given appropriate context. In this workshop you configure the multi-agent system to use Claude as the orchestrating LLM that reads MCP tool descriptions and dispatches tool invocations based on task requirements. The instructor covers Claude-specific MCP integration patterns during the workshop.

MCP is an open protocol that works with any LLM that supports structured tool use. While Anthropic designed MCP and Claude implements it natively, other LLMs that support function calling can also serve as orchestrating agents in an MCP-based system. The workshop covers MCP integration with multiple LLM backends so you can choose the model that best fits each agent's specific requirements.

Anthropic's MCP provides the communication protocol for the context engineering architecture taught in this workshop. Context engineering defines what context each agent needs and how it should be structured. MCP defines how that structured context is communicated between agents. Together they form the foundation of the Glass-Box Context Engine: context engineering provides the what and why, MCP provides the how. Understanding both is what makes production multi-agent AI systems reliable.

Anthropic actively develops and maintains the Model Context Protocol with regular updates to the specification, SDK, and reference implementations. Denis Rothman covers the current state of the protocol and its development trajectory during the workshop, including the features most relevant for production multi-agent AI systems in 2026. All participants receive access to workshop materials that are updated when significant MCP changes affect the patterns taught in the session.