シャドーイング練習: MCP vs API: The protocol every developer needs to know - 動画で英語スピーキングを学ぶ
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If you've ever built an app that talks to an AI model, MCP changes everything.
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Because the way AI connects to your tools, data, and systems is being completely rewritten.
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For years, APIs used to be the glue that held everything together.
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But now there's a new standard rising fast and that something is called the Model Context Protocol or MCP.
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And it might just be the biggest shift since APIs themselves.
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So what exactly is MCP and why are people saying it could replace the way we integrate AI with everything around it?
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Let's break it down clearly so that by the end of this video, you'll understand how MCP actually works and how it's different from APIs
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and why it's reshaping how we build agents and applications that use large language models.
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For decades, API were the universal handshake between systems.
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You define an endpoint, you sent a request, and you got a response back.
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It's clean and predictable.
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And for traditional software, that is perfect.
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But when large language models enter the picture, everything changed.
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Models don't just call one endpoint.
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They might talk to 10 endpoints, they want to chain them together, or even interpret unstructured data.
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and also ask follow -up questions.
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And that means they don't just need access to a tool, they also need context.
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But here's the problem.
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APIs are built for programs talking to programs.
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They are not built for models reasoning over messy, real -world data.
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An API is like a locked cabinet.
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You need to know exactly what drawer to open
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and what shape the key is but a model is trying to understand what's inside the cabinet without clear labels.
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So it doesn't know which function to call or which parameters to pass until you tell it.
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And you have to hard code it sometimes and oftentimes you have to keep explaining over and over again.
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That's where MCP comes in.
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It was designed to make models autonomously discover
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and use tools without the constant hand -holding that we've been doing in prompt engineering.
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So before we dive deeper, let's define both sides clearly.
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APIs are the traditional way software communicates.
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They expose specific endpoints, they accept requests in structured formats like JSON, and they return predictable outputs.
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Developers document them, secure them, and version them.
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But they assume one thing that both sides knows exactly what to expect.
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MCP flips that assumption.
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Instead of the model needing to be manually thought about each endpoint,
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MCP gives the model a standardized way to discover what a tool can do, what kind of inputs it expects,
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and what kind of outputs it returns.
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all through context.
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Think of it like giving a model a live machine -readable map of your API instead of a static instruction manual.
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Now that sounds abstract, so let's make it real.
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Imagine you're building an AI agent that manages support tickets.
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You give it access to Gmail, Notion, and Jira.
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With APIs, you'd write custom code for each integration, handle issues like pagination, auth tokens,
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error cases, rate limits, and also teach the model through long prompts, like, when you want to create a Gira ticket,
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call this endpoint with these fields.
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When you want to reply to an email, call Gmail with this payload.
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But with MCP, you don't need to do any of that.
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Each service like Gmail, Notion, Jira, exposes an MCP compatible interface.
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The model discovers these tools automatically and understands their functions as part of its environment.
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You don't tell it how to do it, you give it the context and it figures it out dynamically.
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That's the core difference between APIs, which are code level contracts between two applications,
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and MCP, which is a semantic protocol between a model and its environment.
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So you're no longer teaching the model which endpoint to hit,
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but you're giving it a structured description of what's available and letting it reason about which tool to use and when.
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It's like giving the model a toolbox instead of forcing it to memorize how each tool works.
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This shift might sound small, but it's actually massive for developers building agentic systems.
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With APIs, the logic of what to call and when to call it lived in your app code.
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With MCP, that logic can actually move into the model's reasoning layer itself.
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You can now build a general purpose agent that can plug into any tool that supports the protocol.
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without having to rewrite code for each integration.
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That's the magic here and its standardization, the same way HTTP made websites interoperable.
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Allowing them to share and use data with each other
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and other systems and letting them work together to perform tasks with minimal human intervention.
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MCP is trying to make AI environments interoperable.
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But let's talk about what's actually happening under the hood.
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An MCP server is a lightweight process that sits next to your service or data source.
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It describes what it can do and what functions it exposes all using JSON schemas.
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The model connects to this server through a standardized interface like WebSocket,
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or HTTP and receives metadata about the available resources.
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Once connected, the model can call these functions directly, not by guessing, but by using the metadata.
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It knows what inputs are required and what each field means and what type of output to expect.
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The beauty is that everything is self -describing.
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You don't have to prompt engineer the schema or reformat responses.
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It's all standardized.
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Compare that to an API where every single integration is bespoke.
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You need a developer to read the docs, map the payloads, and manually wrap the endpoints.
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So MCP abstracts that away.
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This means instead of building 100 custom integrations, you build one MCP interface and every compatible model can use it instantly.
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That's why people are calling MCP the plug and play layer for AI systems.
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Now, this doesn't mean APIs are going away.
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APIs are still the foundation.
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They're how your systems actually function.
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But MCP changes how models access those APIs.
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Think of it like this.
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MCP doesn't replace your backend.
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It replaces the middleware between the model and the API.
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The MCP server acts like a translator, converting your existing APIs into a format that models can understand automatically.
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So instead of saying MCP versus API, it's more like MCP on top of APIs.
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This distinction is key.
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MCP doesn't compete with APIs.
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It actually leverages them.
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But it changes who the client is.
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With API, the client is another program or user.
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With MCP, the client is the model itself.
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And that subtle difference changes everything about how we design integrations.
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Let's zoom out for a bit.
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The rise of MCP is part of a bigger movement towards model -native software architecture.
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For decades, we've built systems for humans and for code.
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Now we're building systems for models.
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And models don't consume REST endpoints the way code does.
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They consume context, which includes structure descriptions, schemas, and examples.
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They do this so that they can reason, plan and act.
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So MCP gives them that in a standardized way.
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That's why developers building agent frameworks are moving in this direction.
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They're realizing that connecting a model to a world of tools isn't about a bigger context window, it's about cleaner protocols.
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And let's be honest, it's not all magic.
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MCP is still pretty new.
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The biggest challenge right now is adoption.
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For MCP to truly work, the ecosystem needs servers, clients, and tools to agree on the same standard.
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Another huge challenge is security and control.
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When models can directly call tools through a protocol, you need clear permission layers.
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You don't want a model accidentally sending an email, deleting a file, or making a huge database change that it wasn't supposed to.
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APIs handle that through authentication, keys, and rate limits.
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MCP needs to bring those guardrails into its protocol layer, which is already happening.
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The spec defines capabilities, scopes, and authentication methods that keep things safe but it's still early days.
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And the final challenge is the developer mindset.
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Most of us grew up in an API world.
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We think in terms of endpoints and routes.
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MCP asks us to think in terms of capabilities and context, to design systems that describe what they can do, not just how to do it.
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That's a huge paradigm shift, but it's worth learning early.
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Here's where it all comes together.
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Think about the moment when HTTP unified the internet.
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Before that, every service had its own protocol, from FTP, Gopher, Telnet, which are all different internet protocols.
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Once the web standardized on HTTP, suddenly everything became interoperable.
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MCP is doing the same thing for AI agents.
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Instead of each company inventing its own plugin format or integration layer,
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MCP provides a single open protocol that any model can understand.
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You build your ConnectShare once and any compliant model can use it.
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That means the future of AI tools will look less like custom integrations and more like a shared ecosystem.
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You will have an MCP server for your product, and any AI like Gemini, Claude, GPT can use it instantly.
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That's the world we're heading towards.
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One where models, not just humans, become first -class users of software.
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So to sum it all up, APIs are not dead.
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They are just evolving.
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APIs were made for deterministic systems one program asking another for data.
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And MCP is made for probabilistic systems, a model reasoning about what it can do.
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So the next time someone says MCP versus API, Just remember, it's not a direct comparison.
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It's a foundation being rebuilt.
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MCP sits one layer above APIs and turning them from static routes into living interfaces that models can actually reason about.
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And as more frameworks adopt it, you'll start seeing a new pattern emerge.
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Instead of hard -coded integrations, we'll build model -aware systems where context tools and reasoning can all live in harmony.
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I hope this video was helpful in explaining the differences between MCPs and APIs.
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To learn more about the model context protocol at a deeper level, check out this next video.
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このレッスンについて
このレッスンでは、MCP（モデルコンテキストプロトコル）とAPIの違いについて学び、英語での理解を深めることを目指します。特に、MCPがどのようにAIとツールを接続する新しい方法であるかについて解説し、それがアプリケーション開発やエージェント構築にどのように影響を与えるかを把握します。YouTubeで英語学習を通じて、英語の発音を良くするための具体的な表現や文脈を身につける機会です。
キーボキャブラリーとフレーズ
- MCP - モデルコンテキストプロトコル
- API - アプリケーションプログラミングインターフェース
- エンドポイント - データ通信の接点
- リクエスト - 要求
- レスポンス - 応答
- コンテキスト - 文脈
- モデル - AIシステム
- 自動発見 - 自動的に見つけること
練習のヒント
この動画は、堅実でありながらスムーズなトーンで進行します。shadow speakの技術を活用し、発音を磨くためには、以下のポイントを考慮しましょう。まず、各セクションが現れる際に、一度Pause（中断）して自分の声で繰り返してみてください。特に、MCPやAPIのような専門用語は、ゆっくり丁寧に発音することが大切です。次に、文のリズムやアクセントに注意を払い、実際の発話速度に合わせて練習してみてください。YouTubeで英語学習を通じて、効果的なshadow speechを武器にしましょう。重要なのは、自分の声がどのように響くかを意識しながら繰り返すプロセスです。この方法により、英語の発音を良くすることができ、より自然な会話スキルを身につけることができるでしょう。
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | is called · are built · are not built |
| 関係詞節 who / which + 節 — 人や物について情報を加える | APIs, which are · MCP, which is · layer, which is |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | you've ever built · we've built |
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。