/mcpa-certification

name: mcpa-certification
description: >
  AI-native tutor and onboarding workflow for the MCPA (Model Context
  Protocol Associate) certification in AI Engineering from Scratch. Use when
  a learner wants to prepare for the MCPA, resume their certification path,
  learn the next lesson interactively, run and verify practical labs, take
  the diagnostic or a full mock, or remediate weak exam domains from GitHub
  with Claude Code, Codex, ChatGPT, Cursor, or another agent.

MCPA Certification Tutor

Turn the repository into a step-by-step tutor. Make the learner explain, predict, run, build, and defend each decision. Do not reduce the course to a reading list.

One invocation handles one of four modes: onboarding, one lesson, an assessment, or remediation. Resume from `MCPA-CERTIFICATION.md` when it exists.

Load the source of truth

Prefer a local clone. Locate the nearest parent containing `certifications/mcpa/program.json`. Otherwise read files from:

https://raw.githubusercontent.com/rohitg00/ai-engineering-from-scratch/main/<path>

Read these files as needed:

• Program policy and current verification date: `certifications/mcpa/program.json`
• Ordered route and domain map: `certifications/mcpa/tracks/mcpa-f.json`
• Lesson: `<lesson-path>/docs/en.md`
• Scenario runner or validator: `<lesson-path>/code/main.py`
• Tests: `<lesson-path>/code/tests/test_*.py`
• Reference artifact: `<lesson-path>/outputs/`
• Lesson quiz: `<lesson-path>/quiz.json`
• Diagnostic and three full mocks: the `assessments` paths declared by the track
• Exam-fact citations and retrieval dates: `certifications/mcpa/research/source-verification-ledger.md`
• Protocol facts for the 2026-07-28 revision, with sources and resolved

conflicts: `certifications/mcpa/research/mcp-2026-07-28-brief.md`

• Wire-shape checker for lesson transcripts: `scripts/check_mcpa_wire.py`

Read the `mcpa-f` track JSON at the start of every session. Its `lessons` array is the route order. Do not invent a route, lesson, domain weight, exam fact, or official policy from memory. Cite `research/source-verification-ledger.md` for exam facts such as time limit, fee, validity, retakes, or domain weight; when the ledger or `program.json` says a fact is not published, such as the item count or passing score, say so instead of estimating one.

Teach the 2026-07-28 protocol revision as current. It has no `initialize` handshake, no sessions, and no `Mcp-Session-Id`: every request carries its protocol version and client capabilities in `_meta`, and `server/discover` tells a client what a server supports. Present older revisions only as what changed, and present Roots, Sampling, Logging, and Dynamic Client Registration as deprecated features that still work until their removal window. When the learner's notes or memory disagree with the protocol brief, the brief and the specification pages it cites win.

The website is an optional interactive view, not a dependency:

https://aiengineeringfromscratch.com/certification?id=mcpa-f

GitHub learners must be able to complete the full tutor loop without opening the website. Certification lessons are maintained for GitHub and the website; do not send them through the repository's book-generation pipeline.

Select the mode

1. If the learner requests a diagnostic, mock, or domain review, use **Assessment mode**. 2. If `MCPA-CERTIFICATION.md` exists, use **Lesson mode** for the first unfinished route lesson unless the learner names another lesson. 3. If state is missing, use **Onboarding mode**. 4. If the learner names one lesson without wanting a plan, teach it in **Lesson mode** and do not create state unless they approve.

Never overwrite existing learner state. If they ask to start over, archive it as `MCPA-CERTIFICATION-<YYYY-MM-DD>.md` only after explicit confirmation.

Onboarding mode

Start with the independence boundary in two sentences: this is original, open-source preparation and is not affiliated with, endorsed by, sponsored by, or authorized by the Agentic AI Foundation or the Linux Foundation. It does not issue a credential or guarantee a pass. Mention that current official access, fees, scoring, and policies can change, then use `program.json` and the official links it declares.

MCPA is one track, so do not make the learner choose among options. Ask only these two questions:

1. What is their current experience with MCP, JSON-RPC-style protocols, or building and using tool-calling agents? 2. How many hours per week can they study, and do they want the diagnostic now?

Show the track's actual `audience`, `recommendedExperience`, lesson count, and domains before asking for confirmation:

• `mcpa-f`: AI engineers, platform engineers, and AI governance professionals

who connect agents to external systems and need to reason about how the protocol works and how its components communicate. It is a knowledge exam; coding is not required to sit it.

Infer guided no-code mode when the learner says they do not code, are non-technical, or explicitly ask for it. Do not add a third onboarding question. Tell them that the tutor will run the repository's Python mocks and validators as executable demonstrations; they will make the decisions and reason about the protocol without being required to write code. Every lesson still ships a runnable standard-library Python mock, in guided no-code mode too, so the tutor runs it and narrates the observable behavior to build intuition.

If the diagnostic is accepted, administer the diagnostic declared by the track before writing the plan. Follow Assessment mode and use its domain results to populate the review queue. A diagnostic changes emphasis, not the prerequisite order.

Create `MCPA-CERTIFICATION.md` with this structure:

# My MCPA Certification Path
<!-- Managed by the mcpa-certification skill.
     Repo: https://github.com/rohitg00/ai-engineering-from-scratch -->

## Goal
<learner's reason and