Which programming languages does this skill support?

The mcp-builder skill supports both Python (using the FastMCP framework) and Node.js/TypeScript (using the official MCP SDK).

What is agent-centric design in the context of MCP?

It is a design philosophy that focuses on building tools around natural human workflows and AI context limitations rather than just mirroring technical API endpoints.

What is an MCP server?

An MCP (Model Context Protocol) server is a specialized service that provides tools and resources allowing LLMs to interact safely and effectively with external APIs, databases, and services.

How does this skill improve tool reliability?

It provides a structured evaluation phase where you create complex, verifiable QA pairs to ensure the LLM can successfully use the tools to solve multi-step, real-world problems.

How do I safely test an MCP server?

The skill recommends using an evaluation harness or running the server in a separate process (like tmux) to prevent the long-running stdio process from hanging your main development environment.

MCP Server Builder

Name: MCP Server Builder
Author: mugubr

bymugubr

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API 개발

Guides the creation of high-quality Model Context Protocol (MCP) servers to enable LLMs to interact with external services and APIs effectively.

소개

The mcp-builder skill provides a comprehensive framework for developing production-ready Model Context Protocol (MCP) servers in Python and TypeScript. It moves beyond simple API wrapping by emphasizing agent-centric design principles, focusing on high-impact workflows, context window optimization, and actionable error handling. Whether you are using FastMCP or the MCP SDK, this skill guides you through the entire lifecycle of tool development—from deep research and implementation of shared utilities to rigorous evaluation using realistic, complex scenarios to ensure your AI agents can perform reliably in the real world.

주요 기능

Standardized implementation patterns for error handling and pagination
Structured workflow for API integration and data model mapping
Agent-centric tool design principles for optimized LLM interaction
Cross-platform support for Python (FastMCP) and TypeScript (SDK)
10 GitHub stars
Comprehensive evaluation framework for verifiable tool performance

사용 사례

Creating robust evaluation suites to test AI tool-calling reliability
Building custom MCP servers to connect internal business APIs to Claude
Optimizing existing REST endpoints for AI-native workflow automation

소개

주요 기능

Standardized implementation patterns for error handling and pagination
Structured workflow for API integration and data model mapping
Agent-centric tool design principles for optimized LLM interaction
Cross-platform support for Python (FastMCP) and TypeScript (SDK)
10 GitHub stars
Comprehensive evaluation framework for verifiable tool performance

사용 사례

Creating robust evaluation suites to test AI tool-calling reliability
Building custom MCP servers to connect internal business APIs to Claude
Optimizing existing REST endpoints for AI-native workflow automation