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TypeScript MCP Server Boilerplate

by bjh5098
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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
calculatorB

두 개의 숫자와 연산자를 입력받아 사칙연산을 수행하고 결과를 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
number1Yes첫 번째 숫자
number2Yes두 번째 숫자
operatorYes연산자 (+, -, *, /)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes계산 결과

TDQS

B3.1/5.0
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 of behavioral disclosure. It mentions performing arithmetic and returning results, but doesn't cover critical behaviors like error handling (e.g., division by zero), performance characteristics, or any side effects. For a tool with no annotations, this leaves significant gaps in understanding how it behaves beyond the basic operation.

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 in Korean that directly states the tool's function without unnecessary words. It's front-loaded with the core action and appropriately sized for a simple calculator tool, with zero waste.

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 low complexity (basic arithmetic), high schema coverage (100%), and presence of an output schema (implied by context signals), the description is somewhat complete but lacks depth. It covers the basic operation but doesn't address potential issues like errors or limitations, which could be important for an AI agent to use it correctly in edge cases.

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 description coverage is 100%, with clear descriptions for all parameters (number1, number2, operator with enum). The description adds minimal value beyond the schema, only implying that the operator is for '사칙연산' (four basic operations), which aligns with the enum. Since the schema does the heavy lifting, 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.

Purpose4/5

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

The description clearly states the tool's purpose: '두 개의 숫자와 연산자를 입력받아 사칙연산을 수행하고 결과를 반환합니다' (Takes two numbers and an operator, performs arithmetic operations, and returns the result). It specifies the verb (performs arithmetic), resource (numbers), and operation type (four basic operations). However, it doesn't explicitly differentiate from sibling tools like 'greet' or 'time' beyond being a calculator function, which is somewhat implied but not stated.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or comparison with sibling tools (e.g., use 'calculator' for math vs. 'get-weather' for weather data). Usage is implied by the purpose but lacks explicit context or exclusions.

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

generate-imageC

텍스트 프롬프트를 입력받아 AI 이미지를 생성합니다. FLUX.1-schnell 모델을 사용합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYes이미지 생성을 위한 텍스트 프롬프트 (영어 권장)

TDQS

C2.9/5.0
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 of behavioral disclosure. It states the tool generates AI images using a specific model, which implies it's a creation/mutation operation (likely not read-only), but doesn't cover critical aspects like rate limits, authentication needs, cost implications, or output format. For a generative tool with zero annotation coverage, this is a significant gap in transparency.

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 extremely concise and front-loaded: two sentences that directly state the tool's function and model used, with zero wasted words. Every sentence earns its place by providing essential information without redundancy or fluff, making it efficient for quick understanding.

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 complexity of an AI image generation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., mutation nature, rate limits), output expectations (e.g., image format, size), and usage constraints. While it specifies the model, more context is needed for the agent to use this tool effectively and safely in varied 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%, with the single parameter 'prompt' fully documented in the schema (type, length constraints, and description recommending English). The description adds no additional parameter semantics beyond what the schema provides, such as prompt formatting tips or model-specific guidelines. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 clearly states the tool's purpose: '텍스트 프롬프트를 입력받아 AI 이미지를 생성합니다' (takes a text prompt as input to generate an AI image). It specifies the action (generate) and resource (AI image), and mentions the specific model (FLUX.1-schnell). However, it doesn't explicitly differentiate from sibling tools like 'calculator' or 'geocode', though the domain is distinct enough that differentiation is implied rather than explicit.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions the model used (FLUX.1-schnell) but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't indicate if this is for creative vs. technical images, or when to prefer this over other image-generation methods. The lack of usage context leaves the agent without explicit direction.

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

geocodeB

도시 이름이나 주소를 입력받아 위도와 경도 좌표를 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
addressYes도시 이름 또는 주소 (예: "서울", "New York", "서울시 강남구")

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes위도와 경도 좌표 정보

TDQS

B3.4/5.0
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 of behavioral disclosure. It states the basic function but lacks details on traits like rate limits, accuracy, data sources, error handling, or authentication needs. This is a significant gap for a tool with no annotation coverage.

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 in Korean that directly states the tool's function without any wasted words. It is appropriately sized and front-loaded, making it easy to understand quickly.

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 (one parameter, no nested objects) and the presence of an output schema (which likely covers return values), the description is mostly complete. However, it lacks behavioral context like error cases or usage limitations, which slightly reduces completeness for a tool with no annotations.

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 description coverage is 100%, so the schema already documents the single parameter 'address' with examples. The description adds no additional meaning beyond what the schema provides, such as formatting details or constraints. Baseline 3 is appropriate when 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 states the tool's purpose with specific verbs and resources: '입력받아' (receives input) and '반환합니다' (returns) for '위도와 경도 좌표' (latitude and longitude coordinates). It distinguishes from siblings like calculator, generate-image, get-weather, greet, and time by focusing on geocoding rather than calculation, image generation, weather data, greeting, or time functions.

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?

The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or specific contexts for usage. For example, it doesn't clarify if this is for single addresses only or if there are limitations compared to other geocoding services.

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

get-weatherC

위도와 경도 좌표, 예보 기간을 입력받아 해당 위치의 현재 날씨와 예보 정보를 제공합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
latitudeYes위도 (-90 ~ 90)
longitudeYes경도 (-180 ~ 180)
forecastDaysNo예보 기간 (일 단위, 기본값: 7일, 최대: 16일)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes날씨 정보

TDQS

C2.9/5.0
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 states what the tool does but lacks critical behavioral details: it doesn't mention whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what happens with invalid coordinates. For a tool that likely calls an external API, this is a significant gap in transparency.

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, efficient sentence that front-loads the core functionality. There's no wasted verbiage or redundant information. However, it could be slightly more structured by separating current weather from forecast information for clarity.

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 that an output schema exists (which presumably defines the return structure), the description doesn't need to explain return values. However, for a tool with no annotations and three parameters, the description is minimally adequate—it states the purpose but lacks behavioral context and usage guidance. The existence of an output schema raises the baseline, but gaps remain.

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%, so the schema already fully documents all three parameters with their types, ranges, and descriptions. The description adds no additional parameter semantics beyond what's in the schema—it merely repeats that coordinates and forecast period are inputs. This meets the baseline for high schema coverage.

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 clearly states the tool's function: it takes latitude/longitude coordinates and forecast period as input and provides current weather and forecast information for that location. The verb '제공합니다' (provides) is specific, and the resource '날씨와 예보 정보' (weather and forecast information) is well-defined. However, it doesn't explicitly differentiate from sibling tools like 'geocode' or 'time', which prevents a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'geocode' (which might provide location data) or 'time' (which might provide time-based information), nor does it specify prerequisites or exclusions. The agent must infer usage context solely from the tool's name and description.

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

B3.2/5.0
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 of behavioral disclosure. It states the tool returns a greeting but doesn't elaborate on any behavioral traits, such as error handling, rate limits, or authentication needs. For a tool with no annotations, this minimal description is insufficient to inform the agent about how the tool behaves beyond its basic function.

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 directly states the tool's function without any unnecessary words. It is front-loaded and clear, making it easy for an agent to parse quickly. Every part of the sentence earns its place by conveying essential information concisely.

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 (simple greeting function), high schema coverage (100%), and the presence of an output schema (which handles return values), the description is reasonably complete. It covers the basic purpose and inputs, though it lacks behavioral details. For this context, it provides enough information for an agent to understand and use the tool effectively, but not exhaustively.

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 description coverage is 100%, meaning the input schema fully documents both parameters ('name' and 'language') with descriptions and an enum for 'language'. The description adds no additional semantic information beyond what the schema provides, such as examples or usage notes. According to the rules, with high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract.

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 clearly states the tool's purpose: it takes a name and language as input and returns a greeting. It specifies the verb ('returns a greeting') and resources ('name and language'), making the function unambiguous. However, it doesn't differentiate from sibling tools like 'calculator' or 'get-weather', which is why it doesn't reach a score of 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, such as when to choose 'greet' over other tools like 'time' or 'geocode'. This lack of usage context leaves the agent without direction on appropriate scenarios for invocation.

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

timeB

타임존을 입력받아 해당 타임존의 현재 시간을 반환합니다.

ParametersJSON Schema
NameRequiredDescriptionDefault
timezoneYesIANA 타임존 이름 (예: Asia/Seoul, America/New_York, Europe/London)

Output Schema

ParametersJSON Schema
NameRequiredDescription
contentYes현재 시간 정보

TDQS

B3.1/5.0
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 of behavioral disclosure. It states the basic function but lacks details on traits like error handling (e.g., invalid timezone inputs), rate limits, authentication needs, or response format. For a tool with no annotations, this is a significant gap in transparency.

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 directly states the tool's function without any fluff. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every word earns its place.

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 low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral details, it doesn't fully compensate for the lack of structured context, leaving gaps in understanding how the tool behaves in practice.

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 description mentions '타임존을 입력받아' (receives a timezone as input), which aligns with the single parameter 'timezone'. However, the input schema already has 100% coverage with a clear description and examples (e.g., Asia/Seoul). The description adds minimal value beyond what the schema provides, so it meets the baseline score for high schema coverage.

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 clearly states the tool's purpose: it takes a timezone as input and returns the current time for that timezone. The verb '반환합니다' (returns) and resource '현재 시간' (current time) are specific. However, it doesn't explicitly differentiate from sibling tools like 'get-weather' which might also involve time-related data, so it doesn't reach the highest score.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, such as when to prefer this over other time-related tools (if any existed) or how it relates to siblings like 'get-weather'. This leaves the agent without usage direction.

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 observedcalculator
    • First observedgenerate-image
    • First observedgeocode
    • First observedget-weather
    • First observedgreet
    • First observedtime
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TDQS

B3.4/5.0
Disambiguation5/5

Each tool has a clearly distinct and non-overlapping purpose: calculator for arithmetic, generate-image for AI image generation, geocode for address-to-coordinates conversion, get-weather for weather data, greet for greetings, and time for timezone-based time. There is no ambiguity or confusion between these tools.

Naming Consistency4/5

The naming is mostly consistent with a verb-noun pattern (e.g., generate-image, get-weather, greet, time), but there are minor deviations: 'calculator' is a noun-only name, and 'geocode' is a verb-only name. These deviations slightly break the pattern but do not severely impact readability.

Tool Count5/5

With 6 tools, the count is well-scoped for a boilerplate server that demonstrates diverse functionalities. Each tool serves a unique purpose, and there are no redundant or unnecessary tools, making the set appropriate for its intended scope.

Completeness4/5

The toolset covers a broad range of common utility functions (arithmetic, image generation, geocoding, weather, greetings, time), but as a boilerplate, it lacks deeper domain-specific coverage. There are no obvious gaps for its general-purpose nature, though it doesn't provide full CRUD operations for any single domain.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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