What can you do with this server?

This is a Vercel-deployed MCP server built on Next.js App Router, offering tools, prompts, and resources accessible via Streamable HTTP transport. 🛠️ Tools * greet – Generate a greeting by providing a name and language (ko or en) * calculate – Perform basic arithmetic (add, subtract, multiply, divide) on two numbers * time – Get the current date and time for a specified timezone (default: Asia/Seoul) * geocode – Look up latitude/longitude coordinates for a city name using Open-Meteo * weather – Retrieve current weather conditions for given coordinates using Open-Meteo * generate-image – Generate images using HuggingFace's FLUX.1-schnell model (requires HF_TOKEN env variable or x-hf-token request header) 📝 Prompts * code-review – Generate a structured, senior-engineer-style code review from provided code 📦 Resources * server-info (mcp://my-mcp-server/info) – Returns server metadata as JSON

How do I use TypeScript MCP Server Boilerplate?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@TypeScript MCP Server Boilerplate calculate 5 + 3" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

TypeScript MCP Server Boilerplate

by kimhailey

Overview Schema Source Related Servers Discussions Admin

TypeScript

Local

my-mcp-server (Vercel · Streamable HTTP)

Next.js App Router 와 mcp-handler 위에서 동작하는 Vercel 배포형 MCP 서버입니다. 기존 StdioServerTransport 기반 로컬 서버를 Streamable HTTP 트랜스포트로 이전하여 임의의 MCP 클라이언트(Cursor, Claude Desktop, MCP Inspector 등) 에서 원격으로 호출할 수 있습니다.

아키텍처

MCP Client (Cursor / Inspector)
       │  Streamable HTTP
       │  + x-hf-token header (선택)
       ▼
Next.js App Router (on Vercel)
  app/api/[transport]/route.ts
       │
       ▼
createMcpHandler ─► McpServer
  ├─ tools: greet / calculate / time / geocode / weather / generate-image
  ├─ prompts: code-review
  └─ resources: server-info

[transport] 동적 세그먼트를 사용해 매 요청마다 핸들러를 새로 만들고, 요청 헤더 (x-hf-token) 와 환경변수(HF_TOKEN) 를 클로저로 캡처해 generate-image 도구에 전달합니다.

Related MCP server: TypeScript MCP Server Boilerplate

프로젝트 구조

.
├── app/
│   ├── api/
│   │   └── [transport]/
│   │       └── route.ts   # MCP HTTP 엔드포인트
│   ├── layout.tsx
│   └── page.tsx           # 안내용 정적 페이지
├── next.config.mjs
├── next-env.d.ts
├── package.json
├── tsconfig.json
└── .env.example

빠른 시작

1. 의존성 설치

npm install

2. 환경변수 설정 (선택)

generate-image 도구를 사용할 계획이라면 .env.example 을 .env.local 로 복사한 뒤 HuggingFace 토큰을 채워 넣습니다.

cp .env.example .env.local
# 그리고 HF_TOKEN=hf_xxx... 로 수정

환경변수가 없어도 클라이언트가 매 요청마다 x-hf-token 헤더를 보내면 동작합니다.

3. 로컬 실행

npm run dev

기본 엔드포인트:

안내 페이지: http://localhost:3000
MCP Streamable HTTP: http://localhost:3000/api/mcp

4. MCP Inspector 로 검증

npx @modelcontextprotocol/inspector@latest

Inspector UI 에서:

Transport: Streamable HTTP
URL: http://localhost:3000/api/mcp
(선택) Custom Headers 에 x-hf-token: hf_xxx... 추가
Connect → List Tools → 각 도구 호출 테스트

클라이언트 연결

Cursor (`./.cursor/mcp.json`)

{
    "mcpServers": {
        "my-mcp-server": {
            "url": "https://<your-app>.vercel.app/api/mcp",
            "headers": {
                "x-hf-token": "hf_xxx..."
            }
        }
    }
}

url 만 있으면 5개 도구(greet/calculate/time/geocode/weather)는 정상 동작합니다. generate-image 를 사용하려면 headers.x-hf-token 또는 서버측 HF_TOKEN 환경변수 중 하나가 반드시 필요합니다.

Streamable HTTP 미지원 클라이언트 (`mcp-remote` 브릿지)

{
    "mcpServers": {
        "my-mcp-server": {
            "command": "npx",
            "args": [
                "-y",
                "mcp-remote",
                "https://<your-app>.vercel.app/api/mcp",
                "--header",
                "x-hf-token:hf_xxx..."
            ]
        }
    }
}

Vercel 배포

방법 1: Vercel CLI

npm i -g vercel
vercel

방법 2: GitHub 연동

GitHub 저장소에 푸시
https://vercel.com/new 에서 저장소 선택
Framework Preset: Next.js (자동 감지)
Environment Variables 에서 (선택) HF_TOKEN 추가
Deploy

배포가 완료되면 https://<your-app>.vercel.app/api/mcp 가 MCP 엔드포인트입니다.

등록된 도구

이름	설명
`greet`	이름과 언어(ko/en)로 인사말 생성
`calculate`	두 숫자 사칙연산 (add/subtract/multiply/divide)
`time`	타임존별 현재 시간
`geocode`	Open-Meteo 로 도시명 → 위경도 조회
`weather`	Open-Meteo 로 위경도 → 현재 날씨 조회
`generate-image`	HuggingFace FLUX.1-schnell 이미지 생성 (`x-hf-token` 또는 `HF_TOKEN`)

프롬프트 / 리소스

프롬프트 code-review: 코드를 입력받아 시니어 엔지니어 관점의 체계적 리뷰 메시지 생성
리소스 server-info (mcp://my-mcp-server/info): 서버 메타데이터(JSON)

HF_TOKEN 처리 정책

x-hf-token 헤더 ──(있으면 사용)──► HuggingFace Inference
       │ (없음)
       ▼
HF_TOKEN 환경변수 ──(있으면 사용)──► HuggingFace Inference
       │ (없음)
       ▼
generate-image 도구가 명확한 에러 메시지 반환

헤더가 우선이므로 동일 배포 인스턴스를 여러 사용자가 각자의 토큰으로 사용할 수 있습니다.

스크립트

npm run dev: 로컬 개발 서버
npm run build: 프로덕션 빌드
npm run start: 빌드 결과 실행
npm run lint: Next.js lint

참고 자료

라이선스

MIT

Available Tools

6 tools

calculateA

두 숫자에 대해 사칙연산(+, -, *, /)을 수행합니다.

ParametersJSON Schema

Name	Required	Description
`a`	Yes	첫 번째 숫자
`b`	Yes	두 번째 숫자
`operation`	Yes	연산 종류: add(+), subtract(-), multiply(*), divide(/)

Output Schema

ParametersJSON Schema

Name	Required	Description
`content`	Yes	계산 결과

TDQS

A3.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does not disclose error handling (e.g., division by zero), precision, or side effects. A simple statement of operations is insufficient for a safe tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that conveys the core functionality without any superfluous words. It is well-front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple tool, full schema coverage, and presence of an output schema, the description is largely complete. However, it could mention division-by-zero handling or result types for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The description adds overall context but does not provide additional meaning beyond the schema. Baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs four arithmetic operations on two numbers, using specific verbs and resources. It distinguishes itself from sibling tools like generate-image, geocode, etc., which serve entirely different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for arithmetic computations but provides no explicit guidance on when to use it versus alternatives or when not to use it. No exclusions or context are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate-imageA

HuggingFace FLUX.1-schnell 모델로 이미지를 생성합니다. HF_TOKEN 환경변수가 필요합니다.

ParametersJSON Schema

Name	Required	Description	Default
`prompt`	Yes	이미지 생성 프롬프트
`num_inference_steps`	No	추론 스텝 수 (1~10, 기본값 4)

TDQS

A3.6/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description bears full burden. It discloses the model and env requirement, but omits details like error behavior if token is missing, rate limits, or output format. The added context is moderate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, front-loaded with key information (model and env requirement). No fluff, but could potentially add more value without sacrificing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low parameter count and full schema coverage, the description is fairly complete but lacks information about output (no output schema) and error handling. It adequately covers prerequisites but falls short on behavioral details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters (prompt, num_inference_steps). The description adds no extra semantic information beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states that the tool generates an image using the HuggingFace FLUX.1-schnell model, clearly identifying the verb (generate) and resource (image/model). Sibling tools are unrelated (calculate, geocode, etc.), so no ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the required HF_TOKEN environment variable but provides no guidance on when to use this tool versus alternatives, nor when not to use it. Since siblings are not image-generation tools, usage is implicitly clear but lacks explicit direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

geocodeA

도시 이름으로 위도·경도 좌표를 조회합니다.

ParametersJSON Schema

Name	Required	Description	Default
`city`	Yes	검색할 도시명 (예: 서울, Tokyo)
`language`	No	결과 언어 (기본값: ko)	ko

Output Schema

ParametersJSON Schema

Name	Required	Description
`content`	Yes

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not mention behaviors such as error handling, rate limits, or authentication. It only states the basic function, leaving significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that delivers the core function. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, the return format is likely documented, but the description lacks information on error scenarios or limitations. It is adequate for a simple lookup but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description repeats the schema's parameter descriptions without adding new details. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action (retrieve) and resource (latitude/longitude coordinates) by city name. It is distinct from sibling tools like weather or calculate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool vs alternatives. The purpose is clear, but there is no mention of context or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

greetA

이름과 언어를 입력하면 인사말을 반환합니다.

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes	인사할 사람의 이름
`language`	No	인사 언어 (기본값: en)	en

Output Schema

ParametersJSON Schema

Name	Required	Description
`content`	Yes	인사말

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It states the core behavior (returns a greeting) but does not disclose details like output formatting, error handling, or whether there are side effects. For a simple tool, this is acceptable but not exceptional.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that covers the tool's purpose without any extraneous words. It is front-loaded with the core action and efficiently communicates the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with a complete input schema and an output schema, the description is sufficient. It explains the function clearly, and the remaining details are covered by the structured fields, leaving no significant gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema provides complete descriptions for both parameters (name, language) with enums and defaults. The description merely restates that they are inputs and does not add additional semantic context beyond the schema, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a greeting based on a name and language, using a specific verb (반환합니다) and a clear resource (greeting). This is distinct from sibling tools like geocode or get-weather, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when a greeting is needed, but it does not explicitly state when to use this tool versus alternatives. Since sibling tools are unrelated, no exclusions are necessary, but explicit guidance is absent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

timeA

현재 날짜와 시간을 반환합니다.

ParametersJSON Schema

Name	Required	Description	Default
`timezone`	No	타임존 (기본값: Asia/Seoul, 예: America/New_York, UTC)	Asia/Seoul

Output Schema

ParametersJSON Schema

Name	Required	Description
`content`	Yes	현재 시간 정보

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavioral details. It only states the basic return value without specifying format, timezone handling beyond parameter, or any side effects. This leaves important behavioral aspects undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that immediately conveys the tool's purpose. Every word is necessary, and there is no redundant or extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity and the existence of an output schema, the description is minimally adequate. However, it lacks details about return format or example usage, which would enhance completeness for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of the single parameter with a description of timezone. The tool description does not add further semantic value beyond what the schema already provides, so a baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns the current date and time, using a specific verb and resource. It is easily distinguishable from sibling tools like calculate or weather, which cover different domains.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use or avoid this tool. However, the purpose is straightforward and implied for any situation needing the current date/time, so it is adequate but lacks explicit guidance or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

weatherB

위도·경도 좌표로 현재 날씨를 조회합니다.

ParametersJSON Schema

Name	Required	Description	Default
`latitude`	Yes	위도
`longitude`	Yes	경도

Output Schema

ParametersJSON Schema

Name	Required	Description
`content`	Yes

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description only states it queries current weather without detailing behavior like error handling, rate limits, or data format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence with clear structure. Efficiently conveys core functionality without extraneous text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present, description need not detail returns. However, lacks edge cases or usage context for a simple weather query.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both parameters. Description adds no additional meaning beyond the schema's '위도' and '경도'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb (조회 = query) and resource (날씨 = weather) with specific input coordinates. Distinct from siblings like geocode and calculate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool or alternatives. Does not mention any prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

6 tool updatesv1.0.0May 28, 2026
- First observedcalculate
- First observedgenerate-image
- First observedgeocode
- First observedgreet
- First observedtime
- First observedweather

Deploy Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.6/5.0

Scored 2026-05-28 07:50 across 6 tools

Disambiguation5/5

Each tool has a clearly distinct purpose (arithmetic, image generation, geocoding, greeting, time, weather), with no overlap or ambiguity in their intended use.

Naming Consistency4/5

Most tool names are single-word verbs (calculate, geocode, greet, time, weather), with one hyphenated compound (generate-image). This is mostly consistent but the hyphenated name is a minor deviation.

Tool Count4/5

With 6 tools, the count is reasonable for a boilerplate example server. It is neither too sparse nor overly heavy for illustrating various capabilities.

Completeness3/5

As a boilerplate, the tool set does not target a specific domain, so completeness is inherently neutral. The tools cover basic utility examples but lack any overarching purpose or desired workflow.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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my-mcp-server (Vercel · Streamable HTTP)

아키텍처

프로젝트 구조

빠른 시작

1. 의존성 설치

2. 환경변수 설정 (선택)

3. 로컬 실행

4. MCP Inspector 로 검증

클라이언트 연결

Cursor (./.cursor/mcp.json)

Streamable HTTP 미지원 클라이언트 (mcp-remote 브릿지)

Vercel 배포

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방법 2: GitHub 연동

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참고 자료

라이선스

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Output Schema

Output Schema

Output Schema

Output Schema

Output Schema

Tool Schema Changelog

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Cursor (`./.cursor/mcp.json`)

Streamable HTTP 미지원 클라이언트 (`mcp-remote` 브릿지)