MCP Academy is the premier free interactive learning platform by Infinity Tech Stack for Anthropic's Model Context Protocol (MCP), now governed by the Linux Foundation. It features 13 modules, 48 lessons, 48 hands-on terminal labs, and 140 quizzes covering MCP servers, clients, tools, Streamable HTTP, OAuth 2.1, and the 2026-07-28 stateless specification with zero sign-up required.

How is Infinity MCP Academy different from official Model Context Protocol documentation?

While modelcontextprotocol.io provides the core technical specifications and basic SDK quickstarts, Infinity Tech Stack's MCP Academy is an independent, 100% free interactive curriculum with 13 modules, 48 lessons, 48 in-browser terminal labs, and 140 quizzes. It teaches real-world server and client development, Streamable HTTP, OAuth 2.1, the 2026-07-28 stateless core specification, multi-server gateways, and enterprise security with zero sign-up required.

How long does MCP Academy take to complete?

MCP Academy contains approximately 4 to 5 hours of interactive training across 13 modules and 48 lessons. Progress is saved locally in your browser so you can learn at your own pace.

Is the MCP Academy free?

Yes. The MCP Academy is free to access, with modules, lessons, quizzes, and labs available without sign-up.

What is the Model Context Protocol (MCP)?

MCP is an open standard (now governed by the Linux Foundation) that provides a universal way for AI models and agents to connect to tools, data sources, and external systems. Think of it as USB-C for AI.

What topics does the MCP Academy cover?

The course covers MCP architecture, server/client development, tools & function calling, resources & prompts, transport layers (stdio, Streamable HTTP), OAuth 2.1 security, production deployment, MCP Gateways & Proxies, Linux Foundation governance, enterprise audit logging, multimodal/audio content, client SDK development, MCP Inspector debugging, integration testing, the official MCP Registry, and real-world case studies from DevOps, CRM, and AI coding assistant deployments.

What is an MCP Gateway?

An MCP Gateway sits between AI clients and multiple MCP servers, providing semantic routing, tool aggregation, token optimization, and centralized observability - similar to an API gateway but for the MCP protocol.

Do I need programming experience?

Basic familiarity with JSON and APIs is helpful but not required. The course starts from fundamentals and builds progressively to advanced production patterns, including hands-on server building tutorials.

How do I debug MCP servers?

The course teaches you to use the MCP Inspector (an official interactive debugging tool), write automated integration tests using the Client SDK, and diagnose common issues like stdout corruption and capability negotiation failures.

Does the MCP Academy include hands-on labs?

Yes. Hands-on terminal labs are embedded in relevant lessons, including objectives, hints, solution commands, and XP rewards.

What is the latest version of MCP?

The current MCP specification revision is 2026-07-28, which shipped after its Release Candidate and made the protocol fully stateless - removing the initialize handshake and protocol-level sessions entirely, adding a mandatory server/discover RPC, and moving Tasks out of the core spec into an official extension. The previous 2025-11-25 revision introduced experimental Tasks, URL-mode elicitation, tool calling in sampling, icon metadata for tools and resources, OpenID Connect Discovery, OAuth Client ID Metadata Documents, and JSON Schema 2020-12 as the default dialect.

MCP Tasks (originally introduced experimentally as SEP-1686 in 2025-11-25 and formalized in 2026-07-28 as the official io.modelcontextprotocol/tasks extension) support long-running, durable operations. Instead of blocking on a single HTTP request, a server returns a task handle that the client polls via tasks/get or updates via tasks/update - essential for agent workflows that run for minutes or hours.

What is the difference between MCP and function calling?

Function calling lets a single model invoke functions you define inline for that one app. MCP is an open, transport-based standard that exposes tools, resources, and prompts from independent servers to any compatible client - so one MCP server works across Claude, ChatGPT, Cursor, and VS Code without rewriting integrations.

How do I build an MCP server?

Scaffold a server with the official Python or TypeScript SDK, register tools (functions), resources (data), and prompts, then expose it over stdio for local use or Streamable HTTP for remote use. The MCP Academy includes hands-on labs that build, connect, and inspect a real server step by step.

What is the best MCP course?

The MCP Academy is a free, interactive course with 13 modules, 48 lessons, 48 hands-on terminal labs, and 140 quiz questions - covering everything from MCP architecture to production security, gateways, the official MCP Registry, and the latest 2026-07-28 stable spec. Progress saves locally with no sign-up required.

Why does the Postgres MCP server use read-only tools?

To prevent the AI from accidentally modifying production data - security through least privilege — Giving an AI write access to a production database is extremely dangerous. Read-only tools follow the principle of least privilege - the AI can query but never modify data.

Why is Datadog the only server using Streamable HTTP transport instead of stdio?

It's a cloud-hosted shared service - Streamable HTTP allows the entire team to connect to one centralized /mcp endpoint — Datadog is a shared cloud service. A single Streamable HTTP MCP server (replacing legacy SSE) serves the entire team over /mcp, while local tools like GitHub CLI run via stdio on each developer's machine.

Case Study: DevOps Pipeline — Real-World Case Studies

← Back to Dashboard

1. Case Study: DevOps Pipeline2. Case Study: Customer Data Platform3. Case Study: AI Coding Assistant

Case Study: DevOps Pipeline

📚 Real-World Case Studies⏱ 12 min⭐ 120 XP⌨ Hands-on lab

MCP-Powered CI/CD Automation

A mid-sized engineering team (40 developers) uses MCP to let their AI coding assistant interact with their entire DevOps stack. Let's analyze the architecture.

System Architecture

┌──────────────────────────────────────────────┐
│          CLAUDE CODE (MCP Host)              │
├──────────────────────────────────────────────┤
│  MCP Clients (one per server):               │
│  ├── GitHub MCP Server (stdio)               │
│  ├── Jira MCP Server (stdio)                 │
│  ├── Datadog MCP Server (Streamable HTTP)    │
│  ├── Postgres MCP Server (stdio, local)      │
│  └── Vercel MCP Server (stdio)               │
└──────────────────────────────────────────────┘

What Each Server Does

Server	Transport	Tools	Resources
GitHub	stdio	create_pr, search_code, list_issues	Repo files, PR diffs
Jira	stdio	create_ticket, update_status, search_issues	Sprint boards, ticket details
Datadog	Streamable HTTP (cloud)	query_metrics, list_alerts, get_logs	Dashboard configs
Postgres	stdio	query (read-only!), list_tables	Schema definitions
Vercel	stdio	deploy, list_deployments, rollback	Environment variables

Real Workflow Example

Developer says: "The checkout page is throwing 500 errors. Find the bug, fix it, and deploy."

Datadog MCP → get_logs(service="checkout", level="error") → Returns stack trace
GitHub MCP → search_code(query="PaymentProcessor.charge") → Finds the file
Claude analyzes the code + error, identifies a null pointer bug
Claude fixes the code via file edit tools
GitHub MCP → create_pr(title="Fix null pointer in checkout")
Vercel MCP → deploy(branch="fix/checkout-null") → Preview deploy
Jira MCP → update_status(ticket="BUG-1234", status="In Review")

Results After 3 Months

Metric	Before MCP	After MCP	Change
Bug investigation time	45 min avg	8 min avg	-82%
Deployment frequency	2/day	8/day	+300%
Context switching (log in to 5 tools)	15 min/incident	0 min	-100%
Developer satisfaction	6.2/10	8.9/10	+44%

💡 Key Insight: The biggest win wasn't speed - it was eliminating context switching. Developers no longer need to log into GitHub, Jira, Datadog, and Vercel separately. Everything happens through one conversation.

⌨ HANDS-ON LABSimulate DevOps Pod Status Tool Call

⭐ +150 XP

Run an MCP server that exposes safe container status checks and pod restart tools.

1Invoke get_pod_status for namespace prod using inspector CLI.

lab-sandbox — simulated environment

INFINITY LAB SANDBOX v2.6 — simulated shell
Type the command for the current objective. Helpers: "hint", "solution", "clear".
$

OBJECTIVE 1 / 1 — type "hint" if stuck

🧪 Knowledge Check

Press 1-4 to select1 of 3

What was the biggest productivity win from the DevOps MCP deployment?

Faster code execution

Eliminating context switching between 5+ different tools

Cheaper API costs

Better code quality