Mcp Builder Ms

# MCP Server Development Guide

When to Use

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.

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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

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# 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 pipelin