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Model Context Protocol — Deep Dive

From protocol basics to production MCP servers. Each chapter: high-level overview + under-the-hood deep dive.
Co-Created by Kiran Shirol and Claude
Core Stack MCP JSON-RPC 2.0 Python SDK TypeScript SDK
home Learning Portal play_arrow Start Learning summarize Key Insights dictionary Glossary 10 chapters · Each with High Level + Under the Hood
Protocol

Architecture & Transports

What MCP is, how it’s structured, and how messages flow between hosts, clients, and servers.
1
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What Is MCP & Why It Exists
The N×M integration problem and how MCP is the “USB-C for AI.”
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2
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Architecture: Hosts, Clients & Servers
The three-layer model, security isolation, and 1:N topology.
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3
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Transports: stdio & Streamable HTTP
Local vs. remote communication, SSE, session management, and reconnection.
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Primitives

Tools, Resources, Prompts & Sampling

The four core capabilities that MCP servers expose to AI applications.
4
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Tools: Letting the Model Act
Tool discovery, JSON Schema, annotations, progress reporting, and change notifications.
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5
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Resources & Resource Templates
URI-based data, text vs. binary, subscriptions, and embedded resources.
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6
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Prompts: Reusable Workflows
Server-defined templates, multi-step workflows, and dynamic prompt generation.
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7
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Sampling & Elicitation
Server-initiated LLM calls, human-in-the-loop via elicitation, and the trust model.
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Production

Security, Building & Ecosystem

OAuth, building servers, and deploying MCP in production with agent frameworks.
8
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Security, Authorization & OAuth
OAuth 2.1 + PKCE, dynamic client registration, roots, and consent scoping.
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9
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Building an MCP Server
Python & TypeScript SDKs, project structure, MCP Inspector, and IDE integration.
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10
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MCP in Production & the Ecosystem
Registry, multi-server composition, agent frameworks, gateways, and the 2026 roadmap.
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