mcp-builder

作者: microsoft

创建高质量MCP(模型上下文协议)服务器的指南,通过精心设计的工具使LLM能够与外部服务交互。在以下情况下使用…

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

来自 microsoft 的更多技能

oss-growth
microsoft
OSS增长黑客角色
official
microsoft-foundry
microsoft
端到端部署、评估和管理Foundry代理:Docker构建、ACR推送、托管/提示代理创建、容器启动、批量评估、持续评估、提示优化工作流、agent.yaml、从追踪中整理数据集。用途:将代理部署到Foundry、托管代理、创建代理、调用代理、评估代理、运行批量评估、持续评估、持续监控、持续评估状态、优化提示、改进提示、提示优化器、优化代理指令、改进代理...
officialdevelopmentdevops
azure-ai
microsoft
用于Azure AI:搜索、语音、OpenAI、文档智能。支持搜索、向量/混合搜索、语音转文字、文字转语音、转录、OCR。适用场景:AI搜索、查询搜索、向量搜索、混合搜索、语义搜索、语音转文字、文字转语音、转录、OCR、文字转语音。
officialdevelopmentapi
azure-deploy
microsoft
对已准备好的应用程序执行Azure部署,这些程序需包含现有的.azure/deployment-plan.md和基础设施文件。当用户要求创建新应用程序时,请勿使用此技能——应改用azure-prepare。此技能运行azd up、azd deploy、terraform apply和az deployment命令,并内置错误恢复机制。需要来自azure-prepare的.azure/deployment-plan.md以及来自azure-validate的已验证状态。适用场景:"运行azd up"、"运行azd deploy"、"执行部署"...
officialdevopsaws
azure-storage
microsoft
Azure存储服务,包括Blob存储、文件共享、队列存储、表存储和Data Lake。解答关于存储访问层(热、冷、冷、归档)的问题,说明各层的使用场景及对比。提供对象存储、SMB文件共享、异步消息传递、NoSQL键值存储和大数据分析。包含生命周期管理。用途:Blob存储、文件共享、队列存储、表存储、Data Lake、上传文件、下载Blob、存储账户、访问层等。
officialdevelopmentdatabase
azure-diagnostics
microsoft
使用AppLens、Azure Monitor、资源健康和安全分类调试Azure生产问题。适用场景:调试生产问题、排查应用服务、应用服务CPU过高、应用服务部署失败、排查容器应用、排查函数、排查AKS、kubectl无法连接、kube-system/CoreDNS故障、Pod挂起、CrashLoop、节点未就绪、升级失败、分析日志、KQL、洞察、镜像拉取失败、冷启动问题、健康探测失败……
officialdevopsdevelopment
azure-prepare
microsoft
为Azure应用准备部署(基础设施Bicep/Terraform、azure.yaml、Dockerfile)。用于创建/现代化或创建+部署;不用于跨云迁移(使用azure-cloud-migrate)。请勿用于:copilot-sdk应用(使用azure-hosted-copilot-sdk)。适用场景:"创建应用"、"构建Web应用"、"创建API"、"创建无服务器HTTP API"、"创建前端"、"创建后端"、"构建服务"、"现代化应用"、"更新应用"、"添加身份验证"、"添加缓存"、"托管在Azure上"、"创建并...
officialdevelopmentdevops
azure-validate
microsoft
部署前对Azure就绪状态进行验证。对配置、基础设施(Bicep或Terraform)、RBAC角色分配、托管标识权限及先决条件进行深度检查,然后再部署。适用场景:验证我的应用、检查部署就绪状态、运行预检、验证配置、检查是否可部署、验证azure.yaml、验证Bicep、部署前测试、排查部署错误、验证Azure Functions、验证函数应用、验证无服务器...
officialdevopstesting