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github/kotlin-mcp-server-생성기

공식 io.modelcontextprotocol:kotlin-sdk 라이브러리를 사용하여 적절한 구조, 의존성 및 구현을 포함한 완전한 Kotlin MCP 서버 프로젝트를 생성합니다.

kotlin-mcp-server-생성기란 무엇인가요?

이 스킬은 Kotlin 기반 MCP(Model Context Protocol) 서버 프로젝트를 처음부터 완전히 생성하는 방법을 제공합니다. 개발자는 복잡한 설정 과정 없이 곧바로 MCP 서버 개발에 집중할 수 있으며, 공식 Kotlin SDK인 `io.modelcontextprotocol:kotlin-sdk`를 활용하여 안정적이고 표준화된 구현이 가능합니다. 주요 지원 플랫폼은 Kotlin/JVM 환경이며, Gradle 빌드 시스템을 사용하여 의존성 관리와 프로젝트 구조를 자동화합니다. 이 스킬은 프로젝트 디렉토리 생성, build.gradle.kts 설정, 기본 서버 클래스 구현, MCP 프로토콜 핸들러 등록 등을 포함한 모든 초기 설정을 처리함으로써 생산성을 크게 향상시킵니다.

지원 대상~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/kotlin-mcp-server-generator

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문서

kotlin-mcp-server-생성기은(는) 무엇을 하나요?

공식 io.modelcontextprotocol:kotlin-sdk 라이브러리를 사용하여 적절한 구조, 의존성 및 구현을 포함한 완전한 Kotlin MCP 서버 프로젝트를 생성합니다.

개요

이 스킬은 Kotlin으로 MCP(Model Context Protocol) 서버를 구축하기 위한 프로젝트 골격을 자동으로 생성합니다. 표준 프로젝트 구조, Gradle 빌드 설정, 기본 MCP 서버 구현을 제공하여 개발자가 신속하게 개발을 시작할 수 있도록 합니다.

사용 방법

다음 명령어로 스킬을 실행하세요:

claude code --skill github/kotlin-mcp-server-generator

또는 대화 중에 직접 스킬을 호출할 수 있습니다:

@github/kotlin-mcp-server-generator를 사용하여 새 Kotlin MCP 서버 프로젝트를 생성해 줘.

생성되는 항목

스킬은 다음 파일들을 포함한 프로젝트를 생성합니다:

  • build.gradle.kts - Kotlin SDK 의존성과 MCP 플러그인이 포함된 Gradle 빌드 파일
  • src/main/kotlin/com/example/mcp/Server.kt - 기본 MCP 서버 구현
  • src/main/kotlin/com/example/mcp/handlers/ - 다양한 핸들러를 위한 디렉토리 구조
  • src/main/resources/application.conf (선택 사항) - 설정 파일

의존성

스킬은 다음 핵심 의존성을 사용합니다:

의존성버전설명
io.modelcontextprotocol:kotlin-sdk최신공식 Kotlin MCP SDK
ch.qos.logback:logback-classic1.4.x로깅
org.jetbrains.kotlinx:kotlinx-coroutines-core1.7.x비동기 지원

예제: 기본 서버 생성

프로젝트가 생성되면 Server.kt 파일은 다음과 유사합니다:

package com.example.mcp

import io.modelcontextprotocol.kotlin.sdk.McpServer
import io.modelcontextprotocol.kotlin.sdk.ServerCapabilities

suspend fun main() {
    val server = McpServer.create(
        serverInfo = ServerInfo(name = "MyKotlinServer", version = "1.0.0"),
        capabilities = ServerCapabilities(
            tools = true,
            resources = true
        )
    )
    
    // 여기에 핸들러 등록
    
    server.start()
}

환경 변수

변수필수설명
MCP_SERVER_NAME아니오서버 이름 (기본값: "MyKotlinServer")
MCP_SERVER_PORT아니오서버 포트 (기본값: 8080)
JAVA_HOME예JDK 17 이상으로 설정

참고 사항

  • JDK 17 이상이 필요합니다.
  • Gradle Wrapper가 프로젝트에 포함됩니다.
  • SDK는 MCP 프로토콜 사양을 완전히 준수합니다.
  • 생성된 프로젝트는 즉시 컴파일 및 실행이 가능합니다.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)

github/agentic-eval

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

github/agent-owasp-compliance

Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"

github/ai-prompt-engineering-safety-review

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.

github/arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.

github/arize-dataset

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

github/arize-evaluator

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

github/arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.

github/arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.

github/arize-link

Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.

github/arize-prompt-optimization

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

github/arize-trace

Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.

github/aspire

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

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