Thomas Piacentini
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Programme de préparation

MCPA Certification Curriculum

Texte original · anglais

Learn the judgment the exam measures by building the protocol the exam describes.

Status: Local preview Guide version: 1.0 Guide effective date: September 2026 Last verified: 2026-09-24

This free curriculum prepares you for the Model Context Protocol Associate (MCPA) exam from the Agentic AI Foundation, delivered through Linux Foundation Training and Certification:

Exam Credential Level Time Fee Core route
MCPA Model Context Protocol Associate Beginner 90 min $250 34 lessons

The exam is online proctored, multiple choice, aligned to the Model Context Protocol specification dated 2026-07-28, valid for two years, with one retake included and a twelve-month eligibility window. The official exam item count and passing score are not published, so this curriculum's three 60-question mocks are original practice sets, not the official length, and practice percentages cannot predict an official outcome. The MCPA page lists 90 minutes; the launch announcement stated 120 minutes. Confirm current pricing, format, duration, and eligibility on the official page before scheduling, because program details can change. Every exam fact and its retrieval date is recorded in research/source-verification-ledger.md.

Learn From GitHub With an AI Tutor

This curriculum is AI-native. Claude Code, Codex, ChatGPT, Cursor, or another agent can teach the route, run the checked-in lab, review the artifact you build, administer the lesson quiz, and resume from saved progress.

Start with the GitHub learner guide, or install the portable certification tutor skill at ../../skills/mcpa-certification/SKILL.md:

bash
npx skills add rohitg00/ai-engineering-from-scratch

Then ask your agent to run:

text
/mcpa-certification

A local Claude Code session discovers the same skill from .claude/skills/ after you clone the repository. Harnesses without slash-command support can read GETTING_STARTED.md and the tutor skill directly. Learner progress lives in MCPA-CERTIFICATION.md; learner work lives under learning-artifacts/mcpa/. The checked-in outputs/ files remain reference artifacts and are never overwritten. This curriculum lives outside the EPUB/PDF book workflow and is not converted into the books.

What You Build

One route, built from first principles, that assembles a working MCP exchange:

Diagram · source
flowchart LR
    F["Fundamentals\nspecification, JSON-RPC, stateless core"] --> A["Architecture\ntopology, discovery, schemas"]
    A --> I["Interactions\nprimitives, multi round-trip, errors, tasks"]
    I --> S["Security\ntrust zones, OAuth, consent, audit"]
    S --> U["Use cases\nroles, extensions, apps, registry"]
    U --> C["Capstone\none 2026-07-28 exchange, end to end"]

Each lesson ships a runnable standard-library MCP mock, a test suite, a lesson quiz, and a reusable artifact. Every lesson teaches the stateless 2026-07-28 revision as current, and scripts/check_mcpa_wire.py checks each lab's transcript for that wire shape. The protocol facts, their primary sources, and the source conflicts resolved while writing live in research/mcp-2026-07-28-brief.md. The route includes a 30-question diagnostic and three full-length original mocks, each with a different emphasis, whose question mix follows the published blueprint weights within practical rounding. They do not imitate or reproduce live exam questions.

The MCPA blueprint has five domains:

Domain Weight
MCP Fundamentals 16%
Architecture and Components 14%
Interactions and Execution 26%
Security and Governance 24%
Use Cases and Ecosystem 20%

GitHub Lesson Index

The tutor reads the track file for route order. This complete index also makes every lesson directly browsable from GitHub.

Not Affiliated

This is an independent community curriculum. It is not affiliated with, endorsed by, sponsored by, or authorized by the Agentic AI Foundation or the Linux Foundation. It does not contain live exam questions. The official exam page and current program policies always take precedence.

Continuer étape par étape.

Code et livrables 7
Ce que vous en retenez

Enregistré dans votre carnet sur cet appareil.

Sources et limites

Les repères de lecture sont déclaratifs. Les résultats de tests sont enregistrés séparément ; ils valent pour les cas exécutés.

AI Engineering from Scratch · 3f9715c · MIT

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