mcp-builder

Build MCP servers in TypeScript and Python with structured tool registration.

288|42|Updated Jan 14, 2026
One-click install
npx skills add https://github.com/shuyu-labs/WebCode --skill mcp-builder-shuyu-labs
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: mcp-builder
Source: https://github.com/shuyu-labs/WebCode/tree/main/skills/claude/mcp-builder
Command: npx skills add https://github.com/shuyu-labs/WebCode --skill mcp-builder-shuyu-labs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Solves the problem of enabling LLMs to interact with external APIs via MCP servers.

Core Features & Use Cases

  • Structured tool registration: Learn to expose tools via the MCP SDK with type-safe input/output validation.
  • Platform versatility: Supports both TypeScript and Python runtimes for MCP servers.
  • Quality-focused workflows: Includes best practices for error handling, pagination, security, and testing.
  • Use Case: Build an MCP server to orchestrate a microservice ecosystem where an LLM can call multiple tools to fetch data, transform it, and assemble a final answer.

Quick Start

Begin by initializing a new MCP server with a descriptive service name and register at least one tool using the SDK. Then run the server using your preferred transport (stdio or streamable HTTP) and validate with a built-in evaluation workflow.

Frequently Asked Questions about mcp-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an MCP server to let an LLM interact with external APIs?

You build an MCP server by initializing a service and using the SDK to register tools with type-safe input/output validation. This enables LLMs to interact with external APIs, fetch data, and orchestrate microservices to assemble answers.

Can I build MCP servers using Python or TypeScript?

Yes, you can build MCP servers using both Python and TypeScript runtimes. The SDK supports platform versatility, allowing you to register tools, validate inputs, and apply best practices in either environment.

What's the best way to validate inputs when registering tools in an MCP server?

The best way to validate inputs when registering tools is to use the MCP SDK's structured tool registration features. This enforces type-safe validation, applies error handling best practices, and ensures structured outputs for AI workflows.

How do I test and evaluate an MCP server before running it in production?

You test and evaluate an MCP server by running it with your preferred transport, such as stdio or streamable HTTP, and validating it with a built-in evaluation workflow. This ensures security, proper pagination, and robust error handling.

Does the MCP SDK support streamable HTTP and stdio transports?

Yes, the MCP SDK supports both streamable HTTP and stdio transports. You can run your MCP server using either method after initializing it and registering at least one tool for your LLM workflows.

When do I need YAML frontmatter metadata in an MCP server?

You need YAML frontmatter metadata in an MCP server when enforcing best practices for tool registration and structured outputs. It helps define tool configurations securely within both TypeScript and Python environments.