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TypeScript MCP Server Boilerplate

by devbrother2024
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TypeScript

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

A3.8/5.0
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 of behavioral disclosure. It states the tool returns a result but does not mention error handling (e.g., division by zero), side effects, or whether the operation is idempotent. For a simple calculator, this is minimally acceptable but lacks richness.

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, efficiently structured sentence that front-loads the essential information. Every element contributes to understanding the tool's function with no redundant or wasted words.

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 tool's low complexity, complete parameter schema coverage, and existence of an output schema (mentioned in context signals), the description is appropriately complete. It successfully conveys the essential operation without needing to detail return values, though mentioning edge cases like division by zero would have improved 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 description coverage is 100%, with all three parameters (a, b, operator) fully documented in the schema. The description summarizes these as 'two numbers and an operator' but does not add syntax details, constraints, or semantic relationships beyond what the schema already provides. Baseline 3 is appropriate for high schema coverage.

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 receives two numbers and an operator, returning arithmetic results. It specifically mentions '사칙연산' (four arithmetic operations), which distinguishes it clearly from image generation, weather, and other sibling tools.

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?

While there are no explicit when-to-use instructions or named alternatives, the specific domain (mathematical calculation) makes the implied usage clear given the unrelated sibling tools (generate-image, geocode, etc.). However, it lacks explicit guidance on when to prefer this over manual calculation or error conditions like division by zero.

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. (FLUX.1-schnell via Together)

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

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes

TDQS

A3.8/5.0
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 disclosure burden. It identifies the specific model (FLUX.1-schnell) and provider (Together), which is valuable context, but omits operational characteristics such as typical latency, rate limits, cost implications, or whether results are persisted vs. ephemeral.

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?

Single efficient sentence with parenthetical model specification. Information is front-loaded with the core action, and every element (API name, model name, provider) earns its place without redundancy.

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 presence of an output schema (handling return value documentation) and complete parameter descriptions, the description provides sufficient essential context by identifying the AI model and backend service. However, it could benefit from noting this is an external API call with potential latency implications.

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%, establishing a baseline of 3. The description mentions 'text prompts' generally but does not elaborate on parameter semantics beyond the schema (e.g., explaining how num_inference_steps affects quality for FLUX specifically or prompt engineering best practices).

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 the tool generates images from text prompts using the HuggingFace Inference API, specifying both the verb (generate) and resource (images). It clearly distinguishes from siblings (calc, geocode, get-weather, etc.) which handle calculations and data retrieval rather than media generation.

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 when-to-use exclusions or alternatives are mentioned. However, the tool's purpose (AI image generation) is distinct enough from text-based/calculation siblings that implied usage is reasonably clear, though explicit guidance on when to prefer this over other image generation methods is absent.

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

geocodeA

도시명 또는 주소를 입력받아 위도·경도 좌표를 반환합니다. (Nominatim OpenStreetMap)

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes검색할 도시명 또는 주소
limitNo반환할 결과 수 (기본값: 1, 최대: 5)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes좌표 결과

TDQS

A3.8/5.0
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 of behavioral disclosure. It successfully identifies the external dependency (Nominatim OpenStreetMap), hinting at network latency and rate limits, but lacks explicit details about error handling, what happens when addresses are not found, or idempotency.

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 with a parenthetical data source attribution. It is appropriately front-loaded with no redundant or wasted text.

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 2-parameter schema with 100% coverage and the presence of an output schema, the description is sufficiently complete for a straightforward geocoding tool. It identifies the return value type (coordinates) and data source, though it could benefit from mentioning error scenarios.

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%, establishing a baseline of 3. The description conceptually maps to the 'query' parameter (도시명 또는 주소) but adds no additional semantic guidance beyond the schema descriptions, such as address formatting tips or the significance of the 'limit' parameter.

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 converts city names or addresses into latitude/longitude coordinates using specific verbs (반환합니다) and identifies the resource (위도·경도 좌표). It distinguishes clearly from unrelated siblings like calc, generate-image, and greet.

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?

While the unique name makes the purpose obvious among siblings, there is no explicit guidance on when to use this versus alternatives or prerequisites. For example, it doesn't mention whether to use this before get-weather when only a city name is available.

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 현재 날씨와 일별 예보를 반환합니다. (Open-Meteo)

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYesWGS84 위도
longitudeYesWGS84 경도
forecast_daysNo예보 일수 (기본값: 3, 최대: 7)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes날씨 정보

TDQS

A3.8/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses the Open-Meteo data source and specifies that both current weather and daily forecasts are returned. However, it omits behavioral details like rate limits, caching behavior, or error handling for invalid coordinates.

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?

Single efficient sentence with action front-loaded. The parenthetical data source '(Open-Meteo)' adds provenance without verbosity. No redundant or filler content.

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 existence of an output schema (per context signals) and 100% input schema coverage, the description appropriately summarizes the return value type (current + daily forecast) without enumerating fields. Adequate for a standard weather lookup tool.

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%, establishing a baseline of 3. The description adds conceptual context by grouping parameters as 'coordinates and forecast period' and explaining they are used to fetch weather data, but does not add syntax details beyond the schema (e.g., WGS84 format is only in 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?

Description clearly states the tool returns current weather and daily forecasts using coordinates and forecast periods. It effectively distinguishes from siblings (calc, generate-image, geocode, greet, time) by specifying the weather domain and Open-Meteo data source.

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?

Description implies usage context (weather lookup by coordinates) but provides no explicit when-to-use guidance versus alternatives. It does not mention coordinate prerequisites or suggest using the 'geocode' sibling tool first if the user only has an address string.

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

greetB

이름과 언어를 입력하면 인사말을 반환합니다.

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

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes인사말

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, placing the full disclosure burden on the description, yet it fails to mention behavioral traits such as whether the operation is read-only, idempotent, or stateless. It also does not clarify that the greeting is generated text rather than fetched from an external service, though the existence of an output schema mitigates the need for return value description.

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 consists of a single, highly efficient sentence that front-loads the core functionality without tautology or redundant phrasing. Every word contributes to understanding the input-output relationship, making it appropriately concise for a simple tool.

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 tool's low complexity (two simple parameters, one required), complete schema coverage, and the existence of an output schema, the description provides sufficient context for correct invocation. However, it could be improved by mentioning safety characteristics or confirming the localized nature of the output given the Korean-language context.

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?

With 100% schema description coverage, the parameters (name and language) are fully documented in the input schema, including the default value for language. The description merely references them ('이름과 언어') without adding semantic context, syntax details, or usage examples beyond what the schema already provides, meeting the baseline for high-coverage schemas.

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

Purpose4/5

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

The description uses the specific verb '반환합니다' (returns) with the resource '인사말' (greeting), clearly stating the tool generates a greeting message when given inputs. It effectively distinguishes itself from siblings like calc, generate-image, and geocode, which perform distinct functions (mathematics, image generation, geocoding).

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 explains what the tool does but does not provide explicit guidance on when to prefer it over alternatives or specific use cases (e.g., when templated greetings are needed). Usage is implied by the tool name and parameter names, but no explicit when/when-not conditions are stated.

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

timeA

현재 시각을 반환합니다. 타임존을 지정할 수 있습니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
timezoneNoIANA 타임존 (기본값: Asia/Seoul)Asia/Seoul

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes현재 시각

TDQS

A3.8/5.0
Behavior3/5

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

Without annotations, the description carries the burden of behavioral disclosure, mentioning timezone support but omitting details about the return format (though mitigated by the presence of an output schema). It does not explicitly confirm this is a safe, idempotent read operation, though this is reasonably inferred from the description's wording.

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 consists of two efficient sentences that front-load the core functionality (returning current time) followed by the key optional feature (timezone specification). 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.

Completeness4/5

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

Given the tool's simplicity (single optional parameter) and the presence of an output schema to define return values, the description provides sufficient context for an agent to understand and invoke the tool correctly. It appropriately delegates parameter details to the schema while conveying the essential purpose.

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?

With 100% schema description coverage, the parameter semantics are adequately handled by the schema itself, which documents the timezone string format and default value. The description adds minimal semantic context beyond stating that timezone specification is possible, meeting the baseline expectation for high-coverage schemas.

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 uses the specific verb '반환합니다' (returns) with the resource '현재 시각' (current time), clearly indicating it retrieves temporal data. This distinctly differentiates it from siblings like calc (calculation), generate-image (image creation), and get-weather (weather data).

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?

While the description implies usage by stating it returns the current time and accepts timezone parameters, it lacks explicit guidance on when to prefer this over manually calculating time or using other tools. No alternative approaches or exclusion criteria are mentioned.

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

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TDQS

A3.7/5.0
Disambiguation5/5

Each tool serves a completely distinct purpose (arithmetic, image generation, geocoding, weather, greeting, time) with no functional overlap. An agent can easily distinguish which tool to use based on the task requirements.

Naming Consistency3/5

Mixed naming conventions exist: some use kebab-case verb-noun (generate-image, get-weather), others use single words (calc, greet, time, geocode). The abbreviation 'calc' instead of 'calculate' and the inconsistent use of prefixes (get-weather vs just weather) reduce predictability.

Tool Count4/5

Six tools is appropriate for a boilerplate/demo server, providing enough variety to demonstrate different integration patterns (external APIs, calculations, utilities) without being overwhelming. Slightly arbitrary collection but reasonable for demonstration purposes.

Completeness3/5

While geocode and get-weather form a cohesive pair, the other tools (calc, generate-image, greet, time) are isolated utilities with no workflow connections. As a boilerplate this demonstrates variety, but as a functional tool set it lacks domain cohesion and specific workflow completion.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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