What is an MCP server?

An MCP (Model Context Protocol) server is a standardized interface that allows Large Language Models to interact with external services, databases, and local tools securely and efficiently.

Does it include testing procedures?

Yes, it includes a dedicated phase for creating realistic evaluation scenarios and quality checklists to ensure your tools perform correctly in complex, multi-step tasks.

How does this skill help with AI context limits?

It provides strategies for returning high-signal information, implementing character limits, and designing concise response formats to make the most of an agent's context window.

Which programming languages are covered in this guide?

This skill provides detailed implementation guidance for both Python (utilizing FastMCP) and Node.js/TypeScript (using the official MCP SDK).

MCP Server Builder

Name: MCP Server Builder
Author: thanhinfore

bythanhinfore

API 开发

Provides a comprehensive workflow and best practices for developing high-quality Model Context Protocol (MCP) servers.

关于

MCP Server Builder is a specialized skill designed to guide developers through the end-to-end process of creating robust Model Context Protocol (MCP) servers. It provides domain-specific knowledge for building tools that allow LLMs to interact with external APIs and services effectively. The skill covers agent-centric design principles, framework-specific implementation patterns for Python and TypeScript, and rigorous evaluation methodologies to ensure tools are reliable, context-efficient, and capable of handling complex real-world workflows.

主要功能

Agent-centric tool design for optimized AI workflows
Python (FastMCP) and TypeScript SDK implementation patterns
0 GitHub stars
Context window optimization and data truncation strategies
Standardized error handling and actionable feedback design
Comprehensive evaluation and testing framework guidelines

使用场景

Creating evaluation harnesses to measure and improve MCP server reliability
Building complex multi-step workflow tools from existing API endpoints
Integrating external service APIs with Claude for real-time data access

关于

主要功能

Agent-centric tool design for optimized AI workflows
Python (FastMCP) and TypeScript SDK implementation patterns
0 GitHub stars
Context window optimization and data truncation strategies
Standardized error handling and actionable feedback design
Comprehensive evaluation and testing framework guidelines

使用场景

Creating evaluation harnesses to measure and improve MCP server reliability
Building complex multi-step workflow tools from existing API endpoints
Integrating external service APIs with Claude for real-time data access