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Use this skill when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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:
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:
MCP Best Practices: 📋 View Best Practices - Core guidelines
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
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.
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)
