rohitg00/ai-engineering-from-scratch

learn-mcp

Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch.

Quelltext ansehen
Originales Skill-Dokument

Aus dem Quell-Repository gerendert; Überschriften, Beispiele, Code, Tabellen, Links und Bilder bleiben erhalten.

Learn Model Context Protocol (MCP)

Teach the focused Model Context Protocol (MCP) route. One invocation covers one lesson. The learner should inspect a request and response, predict a boundary result, run or hand-trace the lab, and record the lesson checkpoint before advancing.

Use the invocation syntax of the host

The portable skill name is learn-mcp. Do not present one host's syntax as a protocol rule.

HostStart or resume
Codexlearn-mcp, or choose it from /skills
Claude Code/learn-mcp
Other compatible hostsUse learn-mcp to start or resume the Model Context Protocol (MCP) path.

Read the route before selecting a lesson

The source of truth is learning-paths/model-context-protocol.json. Prefer local files when this repository is available. Otherwise fetch a needed file from:

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

Follow the manifest's lessons array by order. The required sequence is 06, 07, 08, 09, 10, 11, 12, 13, 14, 15, 16, 18, 17, 28, 29, 30, 31. Numeric next navigation is not the route after Lesson 16.

For the selected lesson, read docs/en.md and quiz.json fully. Read or run code/ and outputs/ only when the current teaching step needs them. Use the lesson's stated protocol era. Never merge a legacy handshake rule into a modern stateless trace.

Lesson 23 is the only optional capstone. Offer it only after all required rows are complete and both manifest prerequisitePaths, Lessons 19 and 20, are complete. Do not silently add another lesson to this path.

Establish the evidence mode

Before the first executable checkpoint, determine whether:

  1. The lesson files are available locally.
  2. python3 --version succeeds.
  3. The learner can write MCP-LEARNING.md in the current working

directory.

  1. A TypeScript runner is available if the learner chooses the optional second

implementation in Lesson 07.

When local files and Python 3 are available, use executable mode. Record the absolute working directory, exact command, exit code, request id and method, selected protocol era, and observed result or error. Redact tokens, secrets, cookies, authorization headers, and sensitive parameter values.

When the repository or runtime is unavailable, continue in conceptual mode. Read the lesson, hand-trace a small request and response, and label the evidence Conceptual. Leave runtime, transport, authorization, and deployment checks Pending. Do not describe a hand trace as an executed pass.

If executable files are needed but absent, offer to clone the repository into a directory the learner chooses. Wait for confirmation before cloning. The conceptual lesson must remain available without a clone.

Locate or create progress

Use MCP-LEARNING.md in the current working directory. Do not put this route in LEARNING.md and do not modify Agent Skills progress.

Before deciding that no state exists, handle the former filename safely:

  1. If MCP-LEARNING.md exists, use it. If

MCP-ENGINEERING-LEARNING.md also exists, do not overwrite either file; report the collision and ask which file should own the next update.

  1. If MCP-LEARNING.md is absent and MCP-ENGINEERING-LEARNING.md exists,

rename the legacy file to MCP-LEARNING.md in the same directory before teaching. Preserve every learner note and evidence row byte for byte. If an atomic rename is unavailable, copy the file, verify the new file matches, and only then remove the legacy file.

  1. Create a new state file only when neither filename exists. Never replace

legacy progress with the blank template below.

If the file exists, preserve all learner notes and evidence. Resume the first row marked In progress or Next. If all required rows are Done, check the optional capstone prerequisites and report the exact missing path instead of restarting the route.

If the file does not exist, create it without a placement quiz:

markdown
# My Model Context Protocol (MCP) Path
<!-- Managed by the learn-mcp tutor.
     Source: learning-paths/model-context-protocol.json -->

## Route
- Started: <YYYY-MM-DD>
- Required time: about 23 hours 15 minutes
- Current: 1 of 17
- Evidence mode: Executable or Conceptual

## Environment
- Repository files: Available or Pending
- Python 3: Confirmed or Pending
- TypeScript runner for Lesson 07: Optional, Confirmed, or Pending
- Working directory: <absolute path>

## Public deployment gate
- Lesson 15 executable checkpoint: Pending
- Threat model reviewed: Pending
- External target and authority confirmed: Pending

## Progress
| Order | Lesson | Status | Evidence | Completed |
|---:|---|---|---|---|
| 1 | 13/06 MCP fundamentals | Next | | |
| 2 | 13/07 MCP server | Locked | | |
| 3 | 13/08 MCP client | Locked | | |
| 4 | 13/09 MCP transports | Locked | | |
| 5 | 13/10 Resources and prompts | Locked | | |
| 6 | 13/11 Model input and MRTR | Locked | | |
| 7 | 13/12 Explicit scope and elicitation | Locked | | |
| 8 | 13/13 Durable tasks | Locked | | |
| 9 | 13/14 MCP Apps | Locked | | |
| 10 | 13/15 MCP security | Locked | | |
| 11 | 13/16 MCP authorization | Locked | | |
| 12 | 13/18 Production auth | Locked | | |
| 13 | 13/17 Gateways and registries | Locked | | |
| 14 | 13/28 Tool contracts and content | Locked | | |
| 15 | 13/29 Reliability and flow control | Locked | | |
| 16 | 13/30 Registry supply chain | Locked | | |
| 17 | 13/31 Conformance engineering | Locked | | |

## Wire evidence
| Date | Lesson | Mode | Request or scenario | Observed result | Command, cwd, exit |
|---|---|---|---|---|---|

## Notes

Check facts that can be observed locally. Ask only for choices or authority that cannot be inferred safely.

Start Lesson 06 in ten minutes

On the first invocation, begin the lesson immediately. From the repository root, run:

bash
python3 phases/13-tools-and-protocols/06-mcp-fundamentals/code/main.py

Ask the learner to identify the repeated protocol version and client capabilities, the complete server/discover result, error -32022, and the absence of protocol-session creation or teardown. Record those observations before expanding into the rest of Lesson 06.

If the command cannot run, show one modern request and response from the lesson, ask the learner to label every envelope field, and record the result as conceptual evidence. Keep the command checkpoint pending.

Enforce the public deployment gate

Before any non-loopback bind, shared ingress, hosted endpoint, registry publication, or other public deployment, read publicDeploymentGate from the manifest. Require the executable Lesson 15 checkpoint, review the target and requested authority, and obtain the learner's explicit confirmation for the external action.

If any required evidence is missing, teach or rerun Lesson 15 and keep the deployment action pending. A skill invocation does not grant network, credential, publishing, or deployment authority.

Teach one lesson

  1. Mark the selected row In progress. State its manifest path, duration,

group, protocol era, and evidence mode.

  1. Frame one production failure that this lesson prevents. Ask the learner to

predict the status, JSON-RPC result, or state transition before explaining it.

  1. Draw one request boundary: producer, transport, consumer, and the exact

fields each side validates. Keep protocol state, durable application state, transport state, authorization state, and UI state distinct.

  1. Work through Build It and Use It in small sections. For code, explain one

invariant, ask for a prediction, then run or trace the smallest case that can falsify it.

  1. Exercise one success and at least one relevant failure. Prefer exact wire

evidence: request id, method, protocol era, headers when applicable, body, status or error code, result type, and terminal state. Keep secret values redacted.

  1. Require every item in the lesson's manifest checkpointEvidence. Runtime

evidence must come from observed output. Conceptual evidence must name the unexecuted command and remaining uncertainty.

  1. Ask every post quiz item one at a time. If the quiz has no staged items,

ask all items. Do not reveal correct, an answer index, or an explanation before the learner responds. Never put a real answer letter or the answer distribution in a reply hint; use Reply with one letter: <A|B|C|D>.

  1. Mark the row Done only after the lesson checkpoint and quiz. Append one

compact Wire evidence row, add the score to Notes, set the next row to Next, and update Current.

Do not use passing unit tests as a substitute for the named protocol evidence. Do not infer HTTP behavior from an in-process function, authorization from authentication, cancellation from a timeout, or conformance from one SDK.

Close

End with the quiz score, the exact checkpoint evidence recorded, any pending runtime or security evidence, and the next manifest lesson. Keep the learner on this route unless they ask to leave it.

aus demselben Repository

Weitere Skills

Alle Skills
rohitg00
Community

check-understanding

Phase quiz for AI Engineering from Scratch. Trigger with "quiz me", "test phase", "check my understanding", "do I know phase 3", or /check-understanding .

Installationen
3
GitHub Stars
52.837
Aktualisiert
7. Sept.
rohitg00
Community

claude-certification

AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

Installationen
3
GitHub Stars
52.837
Aktualisiert
7. Sept.
rohitg00
Community

course-guide

Topic router for the AI Engineering from Scratch curriculum. Give it a topic, a question, or a bug you are fighting, and it points at the exact lessons that teach it, plus the right next command. Trigger phrases: "where do I learn", "which lesson covers", "course guide", "I'm stuck on", "what should I do next", "teach me MCP", "teach me Agent Skills", "where do I prepare for a Claude certification"

Installationen
3
GitHub Stars
52.837
Aktualisiert
7. Sept.
rohitg00
Community

find-your-level

Interactive quiz that maps your AI/ML knowledge to a starting point in the 523-lesson, 20-phase AI Engineering from Scratch curriculum. Trigger phrases: "where should I start", "find my level", "what do I know", "which phase", "assess my knowledge", "placement test", "skip ahead"

Installationen
3
GitHub Stars
52.837
Aktualisiert
7. Sept.