mcp-builder

작성자: microsoft

고품질 MCP(Model Context Protocol) 서버를 생성하기 위한 가이드로, 잘 설계된 도구를 통해 LLM이 외부 서비스와 상호작용할 수 있도록 합니다. 다음 상황에서 사용하세요…

npx skills add https://github.com/microsoft/skills --skill mcp-builder

MCP Server Development Guide

Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.


Microsoft MCP Ecosystem

Microsoft provides extensive MCP infrastructure for Azure and Foundry services. Understanding this ecosystem helps you decide whether to build custom servers or leverage existing ones.

Server Types

TypeTransportUse CaseExample
LocalstdioDesktop apps, single-user, local devAzure MCP Server via NPM/Docker
RemoteStreamable HTTPCloud services, multi-tenant, Agent Servicehttps://mcp.ai.azure.com (Foundry)

Microsoft MCP Servers

Before building a custom server, check if Microsoft already provides one:

ServerTypeDescription
Azure MCPLocal48+ Azure services (Storage, KeyVault, Cosmos, SQL, etc.)
Foundry MCPRemotehttps://mcp.ai.azure.com - Models, deployments, evals, agents
Fabric MCPLocalMicrosoft Fabric APIs, OneLake, item definitions
Playwright MCPLocalBrowser automation and testing
GitHub MCPRemotehttps://api.githubcopilot.com/mcp

Full ecosystem: See 🔷 Microsoft MCP Patterns for complete server catalog and patterns.

When to Use Microsoft vs Custom

ScenarioRecommendation
Azure service integrationUse Azure MCP Server (48 services covered)
AI Foundry agents/evalsUse Foundry MCP remote server
Custom internal APIsBuild custom server (this guide)
Third-party SaaS integrationBuild custom server (this guide)
Extending Azure MCPFollow Microsoft MCP Patterns

Process

🚀 High-Level Workflow

Creating a high-quality MCP server involves four main phases:

Phase 1: Deep Research and Planning

1.1 Understand Modern MCP Design

API Coverage vs. Workflow Tools: Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client—some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.

Tool Naming and Discoverability: Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., github_create_issue, github_list_repos) and action-oriented naming.

Context Management: Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.

Actionable Error Messages: Error messages should guide agents toward solutions with specific suggestions and next steps.

1.2 Study MCP Protocol Documentation

Navigate the MCP specification:

Start with the sitemap to find relevant pages: https://modelcontextprotocol.io/sitemap.xml

Then fetch specific pages with .md suffix for markdown format (e.g., https://modelcontextprotocol.io/specification/draft.md).

Key pages to review:

  • Specification overview and architecture
  • Transport mechanisms (streamable HTTP, stdio)
  • Tool, resource, and prompt definitions

1.3 Study Framework Documentation

Language Selection:

LanguageBest ForSDK
TypeScript (recommended)General MCP servers, broad compatibility@modelcontextprotocol/sdk
PythonData/ML pipelines, FastAPI integrationmcp (FastMCP)
C#/.NETAzure/Microsoft ecosystem, enterpriseMicrosoft.Mcp.Core

Transport Selection:

TransportUse CaseCharacteristics
Streamable HTTPRemote servers, multi-tenant, Agent ServiceStateless, scalable, requires auth
stdioLocal servers, desktop appsSimple, single-user, no network

Load framework documentation:

For TypeScript (recommended):

  • TypeScript SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
  • ⚡ TypeScript Guide - TypeScript patterns and examples

For Python:

  • Python SDK: Use WebFetch to load https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
  • 🐍 Python Guide - Python patterns and examples

For C#/.NET (Microsoft ecosystem):

1.4 Plan Your Implementation

Understand the API: Review the service's API documentation to identify key endpoints, authentication requirements, and data models. Use web search and WebFetch as needed.

Tool Selection: Prioritize comprehensive API coverage. List endpoints to implement, starting with the most common operations.


Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

2.2 Implement Core Infrastructure

Create shared utilities:

  • API client with authentication
  • Error handling helpers
  • Response formatting (JSON/Markdown)
  • Pagination support

2.3 Implement Tools

For each tool:

Input Schema:

  • Use Zod (TypeScript) or Pydantic (Python)
  • Include constraints and clear descriptions
  • Add examples in field descriptions

Output Schema:

  • Define outputSchema where possible for structured data
  • Use structuredContent in tool responses (TypeScript SDK feature)
  • Helps clients understand and process tool outputs

Tool Description:

  • Concise summary of functionality
  • Parameter descriptions
  • Return type schema

Implementation:

  • Async/await for I/O operations
  • Proper error handling with actionable messages
  • Support pagination where applicable
  • Return both text content and structured data when using modern SDKs

Annotations:

  • readOnlyHint: true/false
  • destructiveHint: true/false
  • idempotentHint: true/false
  • openWorldHint: true/false

Phase 3: Review and Test

3.1 Code Quality

Review for:

  • No duplicated code (DRY principle)
  • Consistent error handling
  • Full type coverage
  • Clear tool descriptions

3.2 Build and Test

TypeScript:

  • Run npm run build to verify compilation
  • Test with MCP Inspector: npx @modelcontextprotocol/inspector

Python:

  • Verify syntax: python -m py_compile your_server.py
  • Test with MCP Inspector

See language-specific guides for detailed testing approaches and quality checklists.


Phase 4: Create Evaluations

After implementing your MCP server, create comprehensive evaluations to test its effectiveness.

Load ✅ Evaluation Guide for complete evaluation guidelines.

4.1 Understand Evaluation Purpose

Use evaluations to test whether LLMs can effectively use your MCP server to answer realistic, complex questions.

4.2 Create 10 Evaluation Questions

To create effective evaluations, follow the process outlined in the evaluation guide:

  1. Tool Inspection: List available tools and understand their capabilities
  2. Content Exploration: Use READ-ONLY operations to explore available data
  3. Question Generation: Create 10 complex, realistic questions
  4. Answer Verification: Solve each question yourself to verify answers

4.3 Evaluation Requirements

Ensure each question is:

  • Independent: Not dependent on other questions
  • Read-only: Only non-destructive operations required
  • Complex: Requiring multiple tool calls and deep exploration
  • Realistic: Based on real use cases humans would care about
  • Verifiable: Single, clear answer that can be verified by string comparison
  • Stable: Answer won't change over time

4.4 Output Format

Create an XML file with this structure:

<evaluation>
  <qa_pair>
    <question>Find discussions about AI model launches with animal codenames. One model needed a specific safety designation that uses the format ASL-X. What number X was being determined for the model named after a spotted wild cat?</question>
    <answer>3</answer>
  </qa_pair>
<!-- More qa_pairs... -->
</evaluation>

Reference Files

📚 Documentation Library

Load these resources as needed during development:

Core MCP Documentation (Load First)

  • MCP Protocol: Start with sitemap at https://modelcontextprotocol.io/sitemap.xml, then fetch specific pages with .md suffix
  • 📋 MCP Best Practices - Universal MCP guidelines including:
    • Server and tool naming conventions
    • Response format guidelines (JSON vs Markdown)
    • Pagination best practices
    • Transport selection (streamable HTTP vs stdio)
    • Security and error handling standards

Microsoft MCP Documentation (For Azure/Foundry)

  • 🔷 Microsoft MCP Patterns - Microsoft-specific patterns including:
    • Azure MCP Server architecture (48+ Azure services)
    • C#/.NET command implementation patterns
    • Remote MCP with Foundry Agent Service
    • Authentication (Entra ID, OBO flow, Managed Identity)
    • Testing infrastructure with Bicep templates

SDK Documentation (Load During Phase 1/2)

  • Python SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/python-sdk/main/README.md
  • TypeScript SDK: Fetch from https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/main/README.md
  • Microsoft MCP SDK: See Microsoft MCP Patterns for C#/.NET

Language-Specific Implementation Guides (Load During Phase 2)

  • 🐍 Python Implementation Guide - Complete Python/FastMCP guide with:

    • Server initialization patterns
    • Pydantic model examples
    • Tool registration with @mcp.tool
    • Complete working examples
    • Quality checklist
  • ⚡ TypeScript Implementation Guide - Complete TypeScript guide with:

    • Project structure
    • Zod schema patterns
    • Tool registration with server.registerTool
    • Complete working examples
    • Quality checklist
  • 🔷 Microsoft MCP Patterns - Complete C#/.NET guide with:

    • Command hierarchy (BaseCommand → GlobalCommand → SubscriptionCommand)
    • Naming conventions ({Resource}{Operation}Command)
    • Option handling with .AsRequired() / .AsOptional()
    • Azure Functions remote MCP deployment
    • Live test patterns with Bicep

Evaluation Guide (Load During Phase 4)

  • ✅ Evaluation Guide - Complete evaluation creation guide with:
    • Question creation guidelines
    • Answer verification strategies
    • XML format specifications
    • Example questions and answers
    • Running an evaluation with the provided scripts

microsoft의 다른 스킬

oss-growth
microsoft
OSS 성장 해커 페르소나
official
microsoft-foundry
microsoft
Foundry 에이전트를 엔드투엔드로 배포, 평가 및 관리: Docker 빌드, ACR 푸시, 호스팅/프롬프트 에이전트 생성, 컨테이너 시작, 배치 평가, 지속적 평가, 프롬프트 최적화 워크플로, agent.yaml, 트레이스에서 데이터셋 큐레이션. 용도: Foundry에 에이전트 배포, 호스팅 에이전트, 에이전트 생성, 에이전트 호출, 에이전트 평가, 배치 평가 실행, 지속적 평가, 지속적 모니터링, 지속적 평가 상태, 프롬프트 최적화, 프롬프트 개선, 프롬프트 최적화 도구, 에이전트 지침 최적화, 에이전트 개선...
officialdevelopmentdevops
azure-ai
microsoft
Azure AI: Search, Speech, OpenAI, Document Intelligence에 사용됩니다. 검색, 벡터/하이브리드 검색, 음성-텍스트 변환, 텍스트-음성 변환, 전사, OCR을 지원합니다. 사용 시점: AI Search, 쿼리 검색, 벡터 검색, 하이브리드 검색, 의미 검색, 음성-텍스트 변환, 텍스트-음성 변환, 전사, OCR, 텍스트를 음성으로 변환.
officialdevelopmentapi
azure-deploy
microsoft
이미 준비된 애플리케이션에 대해 기존 .azure/deployment-plan.md 및 인프라 파일이 있는 경우 Azure 배포를 실행합니다. 사용자가 새 애플리케이션 생성을 요청할 때는 이 스킬을 사용하지 말고 azure-prepare를 사용하세요. 이 스킬은 azd up, azd deploy, terraform apply, az deployment 명령을 내장된 오류 복구 기능과 함께 실행합니다. azure-prepare의 .azure/deployment-plan.md와 azure-validate의 검증 상태가 필요합니다. 사용 시점: "run azd up", "run azd deploy", "execute deployment",...
officialdevopsaws
azure-storage
microsoft
Azure Storage Services는 Blob Storage, File Shares, Queue Storage, Table Storage, Data Lake를 포함합니다. 스토리지 액세스 계층(hot, cool, cold, archive), 각 계층 사용 시기 및 계층 비교에 대한 질문에 답변합니다. 객체 스토리지, SMB 파일 공유, 비동기 메시징, NoSQL 키-값, 빅데이터 분석을 제공합니다. 수명 주기 관리를 포함합니다. 사용 용도: blob 스토리지, 파일 공유, 큐 스토리지, 테이블 스토리지, 데이터 레이크, 파일 업로드, blob 다운로드, 스토리지 계정, 액세스 계층,...
officialdevelopmentdatabase
azure-diagnostics
microsoft
Azure에서 AppLens, Azure Monitor, 리소스 상태 및 안전한 트라이지를 사용하여 Azure 프로덕션 문제를 디버그합니다. 사용 시기: 프로덕션 문제 디버그, 앱 서비스 문제 해결, 앱 서비스 높은 CPU, 앱 서비스 배포 실패, 컨테이너 앱 문제 해결, 함수 문제 해결, AKS 문제 해결, kubectl 연결 불가, kube-system/CoreDNS 오류, pod 보류 중, crashloop, 노드 준비 안 됨, 업그레이드 실패, 로그 분석, KQL, 인사이트, 이미지 풀 실패, 콜드 스타트 문제, 상태 프로브 실패,...
officialdevopsdevelopment
azure-prepare
microsoft
Azure 앱을 배포용으로 준비합니다(인프라 Bicep/Terraform, azure.yaml, Dockerfiles). 생성/현대화 또는 생성+배포에 사용하며, 크로스 클라우드 마이그레이션에는 사용하지 않습니다(azure-cloud-migrate 사용). 다음에는 사용하지 마십시오: copilot-sdk 앱(azure-hosted-copilot-sdk 사용). 사용 시점: "앱 생성", "웹 앱 빌드", "API 생성", "서버리스 HTTP API 생성", "프론트엔드 생성", "백엔드 생성", "서비스 빌드", "애플리케이션 현대화", "애플리케이션 업데이트", "인증 추가", "캐싱 추가", "Azure에 호스팅", "생성 및...
officialdevelopmentdevops
azure-validate
microsoft
Azure 배포 전 준비 상태 검증. 구성, 인프라(Bicep 또는 Terraform), RBAC 역할 할당, 관리 ID 권한, 사전 요구 사항에 대한 심층 점검을 실행합니다. 사용 시점: 내 앱 검증, 배포 준비 상태 확인, 사전 점검 실행, 구성 확인, 배포 가능 여부 확인, azure.yaml 검증, Bicep 검증, 배포 전 테스트, 배포 오류 문제 해결, Azure Functions 검증, 함수 앱 검증, 서버리스 검증...
officialdevopstesting