쉐도잉 연습: MCP Just Got a Whole Lot Better - 영상으로 영어 말하기 배우기

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MCP is blowing up right now.

Agents' usage of MCP is increasing exponentially and it's not showing any signs of slowing down.

But at the same time, many agents are now changing the way that they handle tools

and starting to take over responsibilities that used to belong to the server.

So what does all of this mean for the way that we build MCP servers?

Well, let's break it all down.

When MCP first hit the scene, one of the most common approaches for building MCP servers was

to just map each of your API endpoints to its own tool.

And by doing this, an agent was able to access the full range of your API surface area.

And this implementation worked, but it presented two major issues.

First, if your API had hundreds of endpoints, then that meant your MCP server had hundreds of tools.

And all of these tools would be loaded into the context window up front, meaning that before you even started your session, your context window could have been completely bloated by MCP tools.

And secondly, if you had lots of endpoints that were similar to each other, an agent might end up getting confused and not pick the right tool for the job.

In order to solve some of these problems, a lot of MCP servers adopted a layered tool call approach.

So now, instead of listing out hundreds of tool calls, you might just have two or three, You might have a tool to search all of the available endpoints,

a tool to inspect the definition of a specific endpoint, and a tool to execute the request.

This pattern has seen a lot of adoption, but this is now where the big change happens.

Traditionally, this pattern was implemented on the MCP server side.

But more recently, many MCP clients, especially many popular coding agents, are implementing this pattern on the client side.

The pattern is now called progressive tool discovery, and it's a recommended best practice for MCP clients.

In addition, a lot of these agents, like Codex or ClaudeCode, are implementing what's called programmatic tool calling, also known as code mode.

With code mode, instead of calling tools one at a time, the agent will go ahead and write a script that chains those tools together in a sandbox environment.

The script then runs in the sandbox, and then only the final result is returned back to the model.

Between progressive discovery handling tool call selection and then code mode handling tool composition,

the clients are now starting to take more ownership of the problems that servers originally were trying to solve.

So surely this means we can just go back to the

original method of doing a one-to-one mapping of API endpoints to tool calls, right?

Not quite.

While an agent certainly can accomplish everything it needs to just by having access to tools that represent every API endpoint,

that isn't necessarily always the most efficient way to interact with an API.

For any given API, there are often very common tasks or workflows that require stringing together multiple API calls.

And if an agent has to figure out that chain on its own every single time, that's a lot of wasted tokens.

And this is the same reason that SDKs exist.

They give you the raw endpoints but also bundle common workflows into single operations.

And we can apply that same idea to the tools that we expose through our MCP server.

So for example, with Neon, we have this idea of creating a branch.

But it turns out that creating a branch is actually three separate API calls under the hood.

You have to first create the branch itself, but then make a separate call to attach compute resources to the branch, and make a third call to get the connection string.

In the SDK, we bundle those three API calls into one method called createWithCompute.

So as this relates to our MCP server, in order to give MCP clients the most token-efficient path possible,

it's helpful to expose operations like this that they can take advantage of in some of these common use cases.

Of course, if your API is relatively simple and straightforward, a one-to-one mapping might be totally fine.

But for something like Neon, where there's some complexity in provisioning infrastructure,

these ergonomic shortcuts save the agent from making the same mistakes over and over again.

So at Neon, we have an SDK which is generated from from our open API spec.

So that way it covers every API endpoint.

But on top of that generated layer, we also have another thinner layer that adds the ergonomic methods that we were just talking about, things like create with compute.

From here, in order for this to be usable by agents, we need to convert all of these methods into tool calls.

So that's exactly what we did with a package that we call neon tools.

So that includes both the raw endpoints and the ergonomic workflows, all as tool calls.

And by virtue of pulling these tools out into a separate package, we're able to serve a few different use cases.

First and foremost, in the Neon MCP server, the server imports this package to surface all of these tools to any MCP client.

But on top of that, we can also take this package and build adapters around it for different agent frameworks.

So for example, we have adapters for both Mastra and Eve for anyone building custom agents with those frameworks.

And one more thing worth noting is that these tools are organized into categories, so that way you can scope down exactly which tools you want the agent to have access to.

All in all, the big idea here is that the server is controlling what tools are available,

but then the client handles discovering and executing those tools in whatever way it sees fit.

All of this is open source, so if you want to dive through the code and see exactly how everything is implemented, I'll have some links down in the description for you to check out.

ways that you're approaching building your own MCP server?

Let us know down in the comments.

Thank you so much for watching and I'll see you in the next one.

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이 레슨의 어휘와 말하기 포인트

이 C1 수준 말하기 레슨은 영상 “MCP Just Got a Whole Lot Better”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: tool, Mcp, agent, Api, server. 이 영상에는 섀도잉할 문장 61개와 단어 1064개가 있습니다. 말하는 구간의 길이는 5:55입니다. 화자는 분당 약 180단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 81%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에서 가장 어려운 단어 11개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
neon 명사	/ˈniˌɑn/	네온
ergonomic 형용사		인체공학의
workflow 명사	/ˈwɝkfloʊ/	워크플로
sandbox 명사	/ˈsæn(d).bɑks/	사장, 모래상자
token 명사	/ˈtoʊkən/	토큰
bundle 명사	/ˈbʌn.dl̩/	꾸러미, 묶음
execute 동사	/ˈɛksɪˌkjuːt/	처형하다
shortcut 명사	/ˈʃɔːtkʌt/	바로가기
attach 동사	/əˈtæt͡ʃ/	첨부하다, 붙이다
secondly 부사	/ˈsɛk.(ə)ndli/	둘째로
organize 동사	/ˈɔɹɡənaɪz/	조직하다

영상에 나오는 구동사

단어	뜻
blow up 동사	폭파하다

주의할 발음

화자는 I'll, isn't, we're 같은 축약형과 약화된 형태를 5번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, shortcut /ˈʃɔːtkʌt/, speculation /ˌspɛk.jəˈleɪ.ʃən/, accomplish /əˈkɑm.plɪʃ/, traditionally /tɹəˈdɪʃ.ə.nə.li/
긴 단어 — 강세 위치에 주의: exponentially /ɛkspə(ʊ)ˈnɛnʃəli/, programmatic /ˌpɹəʊ.ɡɹəˈmæt.ɪk/, speculation /ˌspɛk.jəˈleɪ.ʃən/, complexity /kəmˈplɛk.sɪ.ti/, traditionally /tɹəˈdɪʃ.ə.nə.li/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: workflow /ˈwɝkfloʊ/, foremost /ˈfɔː.məʊst/, straightforward /ˌstɹeɪtˈfɔː(ɹ)wə(ɹ)d/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: expose /ɪkˈspoʊz/, usable /ˈjuː.zə.bəl/, organize /ˈɔɹɡənaɪz/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, neon, ergonomic, workflow 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: MCP Just Got a Whole Lot Better - 영상으로 영어 말하기 배우기

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