此登錄庫釘選版本所收錄的最新上游作者日期。

microsoft-mcp-builder

已同步 v1.1.0 授權: MIT 來源: microsoft/skills/.github/skills/mcp-builder 提交: cde32f9 收錄的最新變更:

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP), Node/TypeScript (MCP SDK), or C#/.NET (Microsoft MCP SDK).

摘要

用途
提供 MCP 伺服器的完整開發流程,從研究 API、選擇框架與傳輸方式,到實作、測試及評估實用工具。
使用時機
當你要判斷現有的 Microsoft 伺服器是否適用,或規劃並建置連接內部 API 或第三方服務的自訂整合時,請使用此指南。
輸出結果
此指南提供依語言區分的實作指引,並說明如何完成經測試的伺服器及包含十個真實、獨立、唯讀問題且答案經驗證的 XML 評估檔。
上游變更(3 筆,最新收錄 2026-01-28)
f80c38e docs(mcp-builder): add Microsoft MCP patterns and Azure integration 在 MCP builder 指南中新增 Microsoft/Azure/Foundry 伺服器模式及 C#/.NET 實作指引。
a2003b6 docs(skills): standardize all skills to skill-creator guidelines 移除了 mcp-builder 技能前置資訊中的 `license` 欄位。
14224cc feat: Add skill creator scripts and templates for initializing and packaging skills 新增 mcp-builder 技能指南初版,涵蓋 MCP 伺服器的規劃、實作、測試、評估及參考資料。
npx skills add "lettucebo/Skills#v2.2.0@microsoft-mcp-builder" --full-depth

⚠ v2.2.0 發布後即可使用

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

Type Transport Use Case Example
Local stdio Desktop apps, single-user, local dev Azure MCP Server via NPM/Docker
Remote Streamable HTTP Cloud services, multi-tenant, Agent Service https://mcp.ai.azure.com (Foundry)

Microsoft MCP Servers

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

Server Type Description
Azure MCP Local 48+ Azure services (Storage, KeyVault, Cosmos, SQL, etc.)
Foundry MCP Remote https://mcp.ai.azure.com - Models, deployments, evals, agents
Fabric MCP Local Microsoft Fabric APIs, OneLake, item definitions
Playwright MCP Local Browser automation and testing
GitHub MCP Remote https://api.githubcopilot.com/mcp

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

When to Use Microsoft vs Custom

Scenario Recommendation
Azure service integration Use Azure MCP Server (48 services covered)
AI Foundry agents/evals Use Foundry MCP remote server
Custom internal APIs Build custom server (this guide)
Third-party SaaS integration Build custom server (this guide)
Extending Azure MCP Follow 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:

Language Best For SDK
TypeScript (recommended) General MCP servers, broad compatibility @modelcontextprotocol/sdk
Python Data/ML pipelines, FastAPI integration mcp (FastMCP)
C#/.NET Azure/Microsoft ecosystem, enterprise Microsoft.Mcp.Core

Transport Selection:

Transport Use Case Characteristics
Streamable HTTP Remote servers, multi-tenant, Agent Service Stateless, scalable, requires auth
stdio Local servers, desktop apps Simple, 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

歷程

v1.0.0 bootstrap
v1.1.0 baseline-verified cde32f9
v1.1.0 license-refresh cde32f9