Can I test my MCP server with this skill?

Yes, the skill includes a complete evaluation phase that guides you through using the MCP Inspector and generating 10-point testing datasets in XML format.

How does this improve AI tool performance?

It emphasizes clear naming, actionable error messages, and efficient context management, making it easier for LLMs to discover and use the correct tools accurately.

What is an MCP server?

An MCP (Model Context Protocol) server is a standardized interface that allows AI models to securely connect with local or remote data, tools, and services.

Does this skill support both Python and TypeScript?

Yes, it provides dedicated implementation guides for both the TypeScript SDK and the Python FastMCP framework, including project structures and patterns.

Is this skill suitable for beginners building their first tool?

Absolutely. It provides a high-level workflow that covers everything from initial API research to final deployment and quality checking.

MCP Server Builder

Name: MCP Server Builder
Author: jo-minjun

byjo-minjun

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API Development

Guides the creation of high-quality Model Context Protocol (MCP) servers to integrate external APIs and services with AI models.

About

The MCP Server Builder skill provides a comprehensive, standardized workflow for developing robust Model Context Protocol (MCP) servers, enabling seamless communication between LLMs and external services. It offers domain-specific guidance on API coverage, tool discovery, and context management while supporting both TypeScript (SDK) and Python (FastMCP) environments. By following its structured stages—from research and implementation to rigorous evaluation—developers can build discoverable, reliable, and high-performing tools that significantly enhance an AI's ability to execute complex, real-world tasks through external integrations.

Key Features

Context management strategies for efficient LLM data processing
Deep integration with official MCP protocol specifications and SDKs
Automated evaluation framework for testing tool-calling reliability
2 GitHub stars
Standardized MCP server development workflow for TypeScript and Python
Guided tool discovery and naming convention best practices

Use Cases

Generating complex QA evaluation pairs to benchmark AI tool performance
Building custom bridges between internal enterprise APIs and Claude Code
Developing standardized toolsets for AI agents to interact with web services

About

Key Features

Context management strategies for efficient LLM data processing
Deep integration with official MCP protocol specifications and SDKs
Automated evaluation framework for testing tool-calling reliability
2 GitHub stars
Standardized MCP server development workflow for TypeScript and Python
Guided tool discovery and naming convention best practices

Use Cases

Generating complex QA evaluation pairs to benchmark AI tool performance
Building custom bridges between internal enterprise APIs and Claude Code
Developing standardized toolsets for AI agents to interact with web services