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TypeScript MCP Server 보일러플레이트

TypeScript MCP SDK를 활용하여 Model Context Protocol (MCP) 서버를 빠르게 개발할 수 있는 보일러플레이트 프로젝트입니다.

📁 프로젝트 구조

typescript-mcp-server-boilerplate/
├── src/
│   └── index.ts          # MCP 서버 메인 진입점
├── build/                # 컴파일된 JavaScript 파일 (빌드 후 생성)
├── package.json          # 프로젝트 의존성 및 스크립트
├── tsconfig.json         # TypeScript 설정
└── README.md            # 프로젝트 문서

Related MCP server: TypeScript MCP Server Boilerplate

🚀 시작하기

1. 의존성 설치

npm install

2. 서버 이름 설정

src/index.ts 파일에서 서버 이름을 수정하세요:

const server = new McpServer({
    name: 'typescript-mcp-server', // 여기를 원하는 서버 이름으로 변경
    version: '1.0.0',
    // 활성화 하고자 하는 기능 설정
    capabilities: {
        tools: {},
        resources: {}
    }
})

💡 팁: 현재 보일러플레이트에는 이미 계산기와 인사 도구, 그리고 서버 정보 리소스가 예시로 구현되어 있습니다.

3. 빌드

npm run build

4. 실행

node build/index.js

빌드가 성공하면 build/ 디렉토리에 컴파일된 JavaScript 파일이 생성되고, 서버가 MCP 클라이언트의 연결을 대기합니다.

🛠️ 개발 가이드

MCP 도구(Tool) 추가하기

MCP 서버에 새로운 도구를 추가하려면 server.tool() 메서드에 Zod 스키마를 직접 정의하여 등록합니다:

import { z } from 'zod'

// 계산기 도구 추가
server.tool(
    'calculator',
    {
        operation: z
            .enum(['add', 'subtract', 'multiply', 'divide'])
            .describe('수행할 연산 (add, subtract, multiply, divide)'),
        a: z.number().describe('첫 번째 숫자'),
        b: z.number().describe('두 번째 숫자')
    },
    async ({ operation, a, b }) => {
        // 연산 수행
        let result: number
        switch (operation) {
            case 'add':
                result = a + b
                break
            case 'subtract':
                result = a - b
                break
            case 'multiply':
                result = a * b
                break
            case 'divide':
                if (b === 0) throw new Error('0으로 나눌 수 없습니다')
                result = a / b
                break
            default:
                throw new Error('지원하지 않는 연산입니다')
        }

        const operationSymbols = {
            add: '+',
            subtract: '-',
            multiply: '×',
            divide: '÷'
        } as const

        const operationSymbol =
            operationSymbols[operation as keyof typeof operationSymbols]

        return {
            content: [
                {
                    type: 'text',
                    text: `${a} ${operationSymbol} ${b} = ${result}`
                }
            ]
        }
    }
)

더 복잡한 도구 예시

// 날씨 정보 조회 도구
server.tool(
    'get_weather',
    {
        city: z.string().describe('날씨를 조회할 도시명'),
        unit: z
            .enum(['celsius', 'fahrenheit'])
            .optional()
            .default('celsius')
            .describe('온도 단위 (기본값: celsius)')
    },
    async ({ city, unit }) => {
        try {
            // 실제 날씨 API 호출 로직 (예시)
            const weatherData = await fetchWeatherData(city, unit)

            return {
                content: [
                    {
                        type: 'text',
                        text: `${city}의 현재 날씨:
온도: ${weatherData.temperature}°${unit === 'celsius' ? 'C' : 'F'}
날씨: ${weatherData.condition}
습도: ${weatherData.humidity}%
풍속: ${weatherData.windSpeed}km/h`
                    }
                ]
            }
        } catch (error) {
            throw new Error(
                `날씨 정보를 가져올 수 없습니다: ${(error as Error).message}`
            )
        }
    }
)

// 도우미 함수
async function fetchWeatherData(city: string, unit: string) {
    // 실제 날씨 API 호출 구현
    // 여기서는 예시 데이터 반환
    return {
        temperature: unit === 'celsius' ? 22 : 72,
        condition: '맑음',
        humidity: 65,
        windSpeed: 12
    }
}

리소스 추가하기

MCP 서버에 리소스를 추가하여 외부 데이터나 파일에 대한 접근을 제공할 수 있습니다:

// 리소스 등록
server.resource(
    'example-file',
    'file://example.txt',
    {
        name: '예시 텍스트 파일',
        description: '예시 텍스트 파일 설명',
        mimeType: 'text/plain'
    },
    async () => {
        return {
            contents: [
                {
                    uri: 'file://example.txt',
                    mimeType: 'text/plain',
                    text: '예시 파일 내용입니다.'
                }
            ]
        }
    }
)

// 동적 리소스 예시
server.resource(
    'app-settings',
    'config://settings',
    {
        name: '애플리케이션 설정',
        description: '애플리케이션의 현재 설정 정보',
        mimeType: 'application/json'
    },
    async () => {
        const settings = {
            theme: 'dark',
            language: 'ko-KR',
            notifications: true,
            lastUpdated: new Date().toISOString()
        }

        return {
            contents: [
                {
                    uri: 'config://settings',
                    mimeType: 'application/json',
                    text: JSON.stringify(settings, null, 2)
                }
            ]
        }
    }
)

📦 주요 의존성

  • @modelcontextprotocol/sdk: MCP 프로토콜 구현을 위한 공식 SDK

  • zod: TypeScript 우선 스키마 검증 라이브러리

  • typescript: TypeScript 컴파일러

🔧 스크립트

  • npm run build: TypeScript를 JavaScript로 컴파일하고 실행 권한 설정

📋 사용 예시

완전한 서버 예시

import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'
import { z } from 'zod'

// 서버 생성
const server = new McpServer({
    name: 'my-mcp-server',
    version: '1.0.0',
    capabilities: {
        tools: {},
        resources: {}
    }
})

// 간단한 인사 도구
server.tool(
    'greet',
    {
        name: z.string().describe('인사할 사람의 이름'),
        language: z
            .enum(['ko', 'en'])
            .optional()
            .default('ko')
            .describe('인사 언어 (기본값: ko)')
    },
    async ({ name, language }) => {
        const greeting =
            language === 'ko' ? `안녕하세요, ${name}님!` : `Hello, ${name}!`

        return {
            content: [
                {
                    type: 'text',
                    text: greeting
                }
            ]
        }
    }
)

// 시스템 정보 리소스
server.resource(
    'system-info',
    'system://info',
    {
        name: '시스템 정보',
        description: '서버의 현재 상태 및 시스템 정보',
        mimeType: 'application/json'
    },
    async () => {
        const systemInfo = {
            server: 'my-mcp-server',
            version: '1.0.0',
            timestamp: new Date().toISOString(),
            uptime: process.uptime()
        }

        return {
            contents: [
                {
                    uri: 'system://info',
                    mimeType: 'application/json',
                    text: JSON.stringify(systemInfo, null, 2)
                }
            ]
        }
    }
)

// 서버 시작
async function main() {
    const transport = new StdioServerTransport()
    await server.connect(transport)
    console.error('MCP 서버가 시작되었습니다')
}

main().catch(console.error)

🔧 Cursor MCP 연결

개발한 MCP 서버를 Cursor에서 테스트할 수 있습니다:

설정 파일 수정

./.cursor/mcp.json 파일을 편집합니다:

{
    "mcpServers": {
        "typescript-mcp-server": {
            "command": "node",
            "args": ["/ABSOLUTE/PATH/TO/YOUR/PROJECT/build/index.js"]
        }
    }
}

주의: 절대 경로를 사용해야 합니다. pwd 명령어로 현재 경로를 확인하세요.

테스트 명령어

Cursor MCP에서 다음과 같이 테스트해볼 수 있습니다:

  • "5 더하기 3은 얼마야?" (계산기 도구 테스트)

  • "안녕하세요 라고 인사해줘" (인사 도구 테스트)

  • 서버 정보 리소스 조회

🔗 참고 자료

📄 라이선스

MIT

Available Tools

6 tools
calcA

두 숫자와 연산자를 입력받아 사칙연산 결과를 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
aYes첫 번째 숫자
bYes두 번째 숫자
operatorYes연산자 (+, -, *, /)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes계산 결과

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It mentions the core function (returns arithmetic result) but lacks disclosure of edge cases such as division by zero, handling of invalid inputs, or error behavior. This is a significant gap for a mutation-like operation that could fail.

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 gets straight to the point. Every word contributes to the meaning, with no redundancy or filler.

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?

The tool is simple and an output schema exists, so return values are covered. However, the description does not mention potential errors or limitations (e.g., division by zero), which is important for a tool with no annotations. It is adequate but could be more 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% (all parameters have descriptions), so the baseline is 3. The description merely paraphrases the parameters ('두 숫자와 연산자') without adding extra meaning, format details, or clarifications beyond what the schema already provides.

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's function: taking two numbers and an operator and returning the arithmetic result. It uses a specific verb ('입력받아 ... 반환합니다') and identifies the resource (calculator). It also distinguishes itself from sibling tools like geocode or get-weather, which are unrelated.

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

Usage Guidelines4/5

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

The description implies the tool should be used for arithmetic calculations, and sibling tools are unrelated, so there is no ambiguity. However, it does not explicitly state when not to use it or mention any exclusions (e.g., 'for non-arithmetic operations use another tool'), so it falls short of a 5.

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

generate-imageA

HuggingFace Inference API를 사용해 텍스트 프롬프트로 이미지를 생성합니다. (모델: FLUX.1-schnell)

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYes이미지 생성 프롬프트
num_inference_stepsNo추론 스텝 수 (1~10, 기본값: 4)

TDQS

A3.6/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the API and model but lacks any details on output format, latency, rate limits, authentication, or failure behavior. This is a significant gap for a tool that generates an image.

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 includes the essential API and model context without unnecessary fluff. It is well-structured and front-loaded with the main action.

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

Completeness2/5

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

Given the absence of an output schema and annotations, the description should explain what the tool returns (e.g., image URL, binary data). It does not, leaving a key gap for users. The tool's simple nature doesn't excuse this omission.

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 100% coverage with clear descriptions for both parameters (prompt and num_inference_steps). The description adds no additional parameter semantics, so it matches the baseline of 3.

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 generates an image from a text prompt using the HuggingFace Inference API, naming the specific model (FLUX.1-schnell). This verb+resource+scope is specific and distinguishes it from siblings like geocode, get-weather, etc.

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

Usage Guidelines4/5

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

While no explicit alternatives are mentioned, the tool's purpose is clear and distinctly different from its siblings. The description implies usage for image generation, which is contextually unambiguous given the sibling list.

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

geocodeA

도시 이름이나 주소를 입력받아 위도와 경도 좌표를 반환합니다. (Nominatim OpenStreetMap API 사용)

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes검색할 도시명 또는 주소 (예: Seoul, 서울, 1600 Pennsylvania Ave NW Washington DC)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes위도/경도 결과

TDQS

A4/5.0
Behavior3/5

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 discloses that the tool uses the Nominatim OpenStreetMap API, implying an external network dependency, but does not mention rate limits, attribution requirements, or fallback behavior. This is moderate context but not comprehensive.

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 with an appositive clarifying the API. It front-loads the action and result, with no wasted words or redundant detail.

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?

With a low-complexity tool, one fully described parameter, and an output schema (which presumably defines the coordinate structure), the description covers the core use case. It could mention usage policy or rate limits for the external API, but that is optional for basic invocation.

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 description coverage is 100%, with the query parameter fully described including examples. The description adds little beyond the schema—it restates that city names or addresses are accepted—so a 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 the verb 'returns' coordinates (latitude/longitude) from a city name or address, which distinguishes it from sibling tools like get-weather or generate-image. The resource and output are explicit and unambiguous.

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

Usage Guidelines4/5

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

The description implies the tool is for geocoding queries and mentions the underlying API, giving clear context. However, it does not provide explicit when-to-use or when-not-to-use guidance, though no similar sibling tool exists to differentiate against.

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

get-weatherA

위도/경도 좌표와 예보 기간을 입력받아 Open-Meteo API를 통해 현재 날씨와 일별 예보를 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes위치의 위도 (-90 ~ 90)
longitudeYes위치의 경도 (-180 ~ 180)
forecast_daysNo예보 기간 (일, 1~16, 기본값: 7)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes현재 날씨 및 예보 정보

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral disclosure burden. It mentions the use of the Open-Meteo API and the dual output (current + daily), but does not disclose potential limitations like data freshness, error behavior, or rate limits. For a read-only weather tool, this is adequate but not rich.

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, compact sentence that is front-loaded with the core function. Every word adds value with no redundancy or filler.

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?

The tool is simple and the description covers the essential purpose. Since an output schema exists, the need to describe return values is reduced. It doesn't mention edge cases or prerequisites, but the complexity is low, making the description reasonably 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?

The schema covers 100% of parameters with individual descriptions, so the description adds little beyond confirming the role of the forecast period as the forecast duration. Baseline of 3 is appropriate since the schema does the heavy lifting.

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 identifies the tool as a weather data retriever using latitude/longitude and forecast period, specifying it returns both current conditions and daily forecasts. This distinguishes it from sibling tools like geocode or time.

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

Usage Guidelines4/5

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

The description implies the intended use case: obtaining weather for a given location and forecast range. It doesn't explicitly state when not to use it or mention alternatives, but there are no competing weather tools among the siblings, so the context is clear.

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
NameRequiredDescriptionDefault
nameYes인사할 사람의 이름
languageNo인사 언어 (기본값: en)en

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes인사말

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

특정 도시의 현재 시간을 반환합니다. 도시를 지정하지 않으면 전체 도시 목록을 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
cityNo조회할 도시 (seoul, new_york, chicago, denver, los_angeles, all)all

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes현재 시간

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It clearly discloses the two behavioral modes (with city and without city), which is the key behavioral nuance. It does not mention edge cases or errors, but for a simple read-only time lookup, this is adequate.

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 two short sentences, front-loaded with the primary purpose. Every word earns its place, and there is no redundancy.

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?

Given the presence of an output schema and the simplicity of the tool, the description covers all essential aspects: what it does and how the parameter affects behavior. It does not need to explain return values because the output schema handles that.

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

Parameters4/5

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

The schema already fully documents the 'city' parameter with enum values and descriptions (100% coverage). The description adds semantic value by explaining that omitting the city returns the full list, which complements the default='all' in the schema.

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 time for a specific city, using the specific verb '반환합니다' (returns) and resource '특정 도시의 현재 시간' (current time of specific city). It also explains the fallback behavior when no city is specified, which distinguishes it from sibling tools like get-weather.

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

Usage Guidelines4/5

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

The description implies when to use the tool: when you need the current time for a city. It also clarifies the behavior when no city is provided (returns full list). However, it does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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. Dates show when Glama detected each change.

  1. 6 tool updatesv1.0.0
    • First observedcalc
    • First observedgenerate-image
    • First observedgeocode
    • First observedget-weather
    • First observedgreet
    • First observedtime
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TDQS

A4/5.0
Disambiguation5/5

Each tool performs a completely distinct function with no functional overlap: geocoding, weather, image generation, greeting, arithmetic, and time retrieval. An agent can easily select the correct tool based on the task.

Naming Consistency4/5

Tool names are all lowercase and use hyphens for multi-word names, but there is a mix of single verbs (geocode, greet, calc, time) and verb-noun compounds (get-weather, generate-image). The pattern is mostly consistent but not uniform.

Tool Count5/5

With 6 tools, the count is well within the ideal range for a general-purpose utility server. Each tool serves a unique, practical purpose, and the set is neither bloated nor too thin.

Completeness4/5

The tools are self-contained and each fully covers its individual function. Since the server appears to be a general utility/demo toolkit with no specific domain, there are no obvious gaps in lifecycle or CRUD operations, though the set lacks a unifying theme.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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