How does it help with tool design?

It teaches agent-centric design, focusing on building tools for entire workflows rather than just wrapping API endpoints, ensuring they are optimized for AI context limits.

Does this skill support Python and TypeScript?

Yes, it provides detailed implementation guides for both Python (using FastMCP and Pydantic) and Node/TypeScript (using Zod and the official SDK).

Can I use this for secure API integrations?

Yes, the skill includes guidance on handling authentication, rate limiting, and creating actionable error messages for secure and reliable API interactions.

What is an MCP server?

An MCP (Model Context Protocol) server is a standardized interface that allows Large Language Models to securely interact with external data sources and tools.

What is Evaluation-Driven Development in this context?

It is a process where you create realistic, complex, and verifiable test questions to ensure your MCP tools actually enable an LLM to solve real-world tasks effectively.

MCP Server Builder

Name: MCP Server Builder
Author: ammosu

byammosu

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API Development

Develops high-quality Model Context Protocol (MCP) servers to bridge AI models with external APIs and services through well-designed tools.

This skill provides a comprehensive, four-phase workflow for building robust MCP servers using Python (FastMCP) or Node/TypeScript. It guides developers through agent-centric design principles, API research, systematic tool implementation with Pydantic or Zod validation, and rigorous evaluation-driven development. By following this guide, users can create high-signal tools that allow AI agents to navigate complex external workflows while respecting context window constraints and providing actionable error feedback.

Key Features

01Comprehensive MCP server development lifecycle from planning to evaluation

02Dual-language support for Python (FastMCP) and Node/TypeScript SDKs

03Evaluation framework for creating verifiable, complex test scenarios

04Agent-centric design patterns for optimized AI tool interaction

051 GitHub stars

06Standardized error handling and response formatting (JSON/Markdown) guidelines

Use Cases

01Building custom data connectors for LLM-driven research and automation

02Integrating proprietary business APIs into Claude's toolset

03Developing standardized toolkits for multi-agent workflows

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ammosu/awesome-claude-skills-zh-tw mcp-builder

For use in Claude.ai and ChatGPT

Download Skill