mcp-builder-ms – skill Agent & Tự động hóa cho agent AI

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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The emoji-named implementation/evaluation guides below are not bundled in this tree. Resolve official SDK documentation rather than claiming those guides or scripts were read. Endpoint/service counts are examples and require current verification. stdio is a transport: its server can still make network requests. Tool annotations are hints, not authorization controls. Enforce tenant/identity checks, least privilege, input/output limits, timeouts and redacted errors in server code; remote services need the protocol’s supported authentication and request-origin protections. Never log protocol messages to stdout on stdio.

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 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 local process transport; server may access networks |

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
• 🐍 Python Guide - Python patterns and examples

For C#/.NET (Microsoft ecosystem):

• 🔷 Microsoft MCP Patterns - C# patterns, Azure MCP architecture, command hierarchy

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.

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Phase 2: Implementation

2.1 Set Up Project Structure

See language-specific guides for project setup:

• ⚡ TypeScript Guide - Project structure, package.json, tsconfig.json
• 🐍 Python Guide - Module organization, dependencies
• 🔷 Microsoft MCP Patterns - C# project structure, command hierarchy

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

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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.

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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:

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