Learn Mcp

作者 rohitg007a181b46332d無授權條款65K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch. Start or resume this route when a learner wants to build, secure, debug, verify, or operate MCP clients, servers, transports, gateways, registries, or conformance gates. Teaches one lesson per invocation and records wire evidence in MCP-LEARNING.md.

AI 產生的概覽

以每次一課的互動輔導方式教授模型上下文協定(MCP)學習路徑,並記錄進度與協定層證據。

功能
此技能依照 AI Engineering from Scratch 課程中的模型上下文協定(MCP)內容,每次呼叫只教授一課。它會讀取路徑清單,透過預測、請求邊界梳理以及成功與失敗練習來講解課程,接著對學習者進行測驗。它會在工作目錄中維護 MCP-LEARNING.md 檔案,記錄路徑進度、環境狀態和協定層證據表,並在任何外部部署前執行公開部署閘門。
適用情境
當學習者想開始或繼續一條結構化學習路徑,以建置、強化、除錯、驗證或維運 MCP 用戶端、伺服器、傳輸、閘道、登錄檔或一致性閘門時使用。它適合有引導、有檢查點的學習,而非一次性的參考問答。
執行需求
需要路徑清單和課程檔案(本機或從倉庫網址取得),可執行模式還需要 Python 3;某一課可選需要 TypeScript 執行環境。它會在当前工作目錄寫入 MCP-LEARNING.md,並可能在取得學習者確認後提出複製倉庫。此技能不附帶指令碼。

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.
  4. 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.
  2. 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.
  3. 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.
  2. Frame one production failure that this lesson prevents. Ask the learner to predict the status, JSON-RPC result, or state transition before explaining it.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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>.
  8. 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.

來源與署名

來源:rohitg00/ai-engineering-from-scratch位於skills/learn-mcp提交7a181b4

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