AI Agents with MCP: Model Context Protocol for Building Clients, Services, and End-to-End Agents

AI Agents with MCP — Model Context Protocol for Building Clients, Services, and End-to-End Agents Kyle Stratis · O'Reilly Media

Anthropic released the Model Context Protocol in late 2024, and within a year it had become the way agents reach tools. This is the first comprehensive treatment of the protocol — not a blog-post tour, but a systems-level account of how MCP servers, clients, and transports actually fit together.

Stratis builds the whole stack in Python: the protocol structure itself, complete servers, working clients, and the transport layers underneath. He is equally clear about what MCP does not do, which is rarer and more useful than another enthusiastic overview.

What you'll take from it:

• The structure and core concepts of the Model Context Protocol
• Building complete MCP servers, clients, and transport layers in Python
• Consuming tools, prompts, and data through MCP-based agent workflows
• Extending agent capabilities for large-scale, AI-native systems

We build on MCP every day across the Tollbooth DPYC™ ecosystem — every paid tool call, every operator service, every Nostr-signed credential moves over this protocol. If you intend to build servers that others will actually pay to call, start here.

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