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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

A3.7/5.0
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. (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.

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

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.

Tool Schema Changelog

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

  1. 6 tool updatesv1.0.0
    • First observedcalc
    • First observedgenerate-image
    • First observedgeocode
    • First observedget-weather
    • First observedgreet
    • First observedtime
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a completely distinct purpose: greeting, geocoding, weather, image generation, arithmetic, and time. There is no overlap or ambiguity between the tool boundaries.

Naming Consistency2/5

Naming is inconsistent: bare verbs like 'greet' and 'calc', single words like 'geocode' and 'time', and hyphenated phrases like 'get-weather' and 'generate-image' are mixed together. There is no predictable verb_noun or unified style.

Tool Count5/5

Six tools is a well-scoped size for a boilerplate demo server. Each tool demonstrates a different capability without excessive overlap or bloat.

Completeness3/5

As a boilerplate/demo set, the tools cover a variety of common MCP integration patterns, but the set has no coherent domain or lifecycle model. There are no CRUD/resource-style tools, which is a notable gap for a general-purpose server template.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

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