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

•

API 开发

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

关于

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.

主要功能

Comprehensive MCP server development lifecycle from planning to evaluation
Dual-language support for Python (FastMCP) and Node/TypeScript SDKs
Evaluation framework for creating verifiable, complex test scenarios
Agent-centric design patterns for optimized AI tool interaction
1 GitHub stars
Standardized error handling and response formatting (JSON/Markdown) guidelines

使用场景

Building custom data connectors for LLM-driven research and automation
Integrating proprietary business APIs into Claude's toolset
Developing standardized toolkits for multi-agent workflows

关于

主要功能

Comprehensive MCP server development lifecycle from planning to evaluation
Dual-language support for Python (FastMCP) and Node/TypeScript SDKs
Evaluation framework for creating verifiable, complex test scenarios
Agent-centric design patterns for optimized AI tool interaction
1 GitHub stars
Standardized error handling and response formatting (JSON/Markdown) guidelines

使用场景

Building custom data connectors for LLM-driven research and automation
Integrating proprietary business APIs into Claude's toolset
Developing standardized toolkits for multi-agent workflows