How do I test the MCP servers I build with this skill?

The skill includes an evaluation phase that guides you in creating 10 complex, realistic QA pairs to verify that your tools can be correctly utilized by an LLM to solve tasks.

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is an open standard that enables LLMs to safely and effectively interact with external data sources and tools through standardized server implementations.

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

It is a design philosophy that focuses on building tools around human workflows and AI context limitations, rather than simply mirroring existing technical API structures.

Does this skill support both Python and Node.js?

Yes, it provides specific implementation guides and best practices for both Python (using FastMCP) and Node/TypeScript (using the official MCP SDK).

MCP Server Builder

Name: MCP Server Builder
Author: ai-wes

byai-wes

API 开发

Guides the creation of high-quality Model Context Protocol (MCP) servers for seamless LLM integration with external services.

关于

The MCP Server Builder skill provides a comprehensive, four-phase framework for developing robust Model Context Protocol servers in Python or TypeScript. It emphasizes agent-centric design principles, ensuring that tools are built for specific workflows rather than just wrapping API endpoints. Developers are guided through deep research, protocol study, implementation using FastMCP or Node SDKs, and a rigorous evaluation process to ensure AI agents can effectively navigate external data and services within limited context windows.

主要功能

Context-aware response formatting including Markdown and JSON options
Comprehensive implementation workflows for Python and TypeScript SDKs
0 GitHub stars
Evaluation framework for verifying tool effectiveness with QA pairs
Detailed error handling strategies for actionable AI feedback
Agent-centric tool design optimization for better LLM performance

使用场景

Optimizing third-party service integrations for AI context efficiency
Building custom toolsets to connect Claude to internal company APIs
Standardizing MCP server development across engineering teams

关于

主要功能

Context-aware response formatting including Markdown and JSON options
Comprehensive implementation workflows for Python and TypeScript SDKs
0 GitHub stars
Evaluation framework for verifying tool effectiveness with QA pairs
Detailed error handling strategies for actionable AI feedback
Agent-centric tool design optimization for better LLM performance

使用场景

Optimizing third-party service integrations for AI context efficiency
Building custom toolsets to connect Claude to internal company APIs
Standardizing MCP server development across engineering teams