Understand, build, test, secure, and operate MCP integrations that connect AI systems to real tools and data using the current protocol.
A rigorous practical course on the interoperable protocol layer connecting AI systems to real tools and data. Learners work from the current stable MCP specification, 2026-07-28, and use a current Tier 1 SDK implementation track—TypeScript, Python, Go, or C#—rather than copying version-fragile tutorials. The course teaches current behavior first: a stateless protocol core, self-describing requests, optional server/discover discovery, header-based routing, current capability contracts, Streamable HTTP and stdio deployment choices, Multi Round-Trip Requests, cacheable deterministic list results, authorization hardening, extensions, testing, conformance, deployment, and migration. Historical initialize/initialized handshakes, protocol session IDs, legacy HTTP+SSE, Dynamic Client Registration, and deprecated Roots, Sampling, and protocol Logging are addressed only as compatibility or migration concerns. Mastery threshold: 80%.
What you will be able to do
- Diagram and explain the current MCP 2026-07-28 architecture, stateless request/response lifecycle, role boundaries, discovery process, transport behavior, version negotiation, and compatibility implications.
- Select MCP, a direct API, or an ordinary application integration as the appropriate boundary for a given use case and defend the choice with explicit interoperability, control, and operational criteria.
- Design and implement a bounded MCP server with a current Tier 1 SDK, precise capability contracts, JSON Schema 2020-12 validation, predictable errors, constrained side effects, and documented version assumptions.
- Connect an MCP client or host to local and remote servers using current discovery, routing, authorization, credential-handling, permission, and approval patterns.
- Threat-model and harden MCP integrations against excessive authority, injection, confused-deputy behavior, credential leakage, malformed data, unsafe retries, and unverified side effects.
- Build server and client tests, inspect protocol traces, run applicable official conformance checks, diagnose version mismatches, and establish observability and release gates.
- Deploy and operate stateless MCP components with appropriate routing, caching, rate limits, fallbacks, ownership, migration procedures, and incident runbooks.
- Build, defend, deploy, and hand off a complete MCP integration that meets an 80% mastery threshold and contributes a complete package to an MCP Systems Build Portfolio.
The AI Agentic School Decision Lab
Don’t just read the protocol — design the integration and defend the decisions. Each module ends with a realistic MCP brief. You create a concrete server/client architecture, capability contract, authorization or security decision, test/migration artifact, or deployment recommendation, explain your rationale, receive rigorous rubric-based coaching and a simulated stakeholder challenge, then revise until you demonstrate 80% mastery. Your best module submissions become your personal MCP Systems Build Portfolio.
The curriculum is source-traceable and human-reviewed before publication. AI is used where it adds learner value—as a research tool and interactive practice/evaluation partner—not as a claimed standards body, security auditor, employer, or accreditation body.