Which programming languages does this skill support?

The skill provides specialized guidance for both Python (using the FastMCP framework) and Node.js/TypeScript (using the official MCP SDK).

What is an MCP server?

An MCP (Model Context Protocol) server is a standardized interface that allows Large Language Models like Claude to interact with external tools, databases, and APIs safely and effectively.

Does this skill include testing protocols?

Yes, it includes a dedicated phase for creating evaluation scenarios and QA pairs to verify that the LLM can successfully use your tools to solve complex, realistic tasks.

How does this help with AI agent performance?

It emphasizes agent-centric design, ensuring tools return high-signal data rather than data dumps and provide actionable error messages that help agents self-correct.

MCP Server Builder

Name: MCP Server Builder
Author: bkootaz

bybkootaz

API Development

Guides developers through building, optimizing, and evaluating high-quality Model Context Protocol (MCP) servers for LLM tool integration.

About

This skill provides a comprehensive workflow for creating high-quality MCP servers that allow Claude and other LLMs to interact seamlessly with external services and APIs. It covers agent-centric design principles, implementation strategies using Python (FastMCP) or Node/TypeScript, and rigorous evaluation methods. By focusing on workflow-oriented tools rather than simple API wrappers, the skill ensures that built integrations provide high-signal information within limited context windows, ultimately improving the reliability and capability of AI-driven applications.

Key Features

Dual-language implementation guides for Python and TypeScript SDKs
Automated schema validation strategies using Pydantic and Zod
Agent-centric tool design principles for optimized context usage
0 GitHub stars
Standardized error handling patterns for actionable agent feedback
Built-in evaluation framework for testing tool performance

Use Cases

Developing standard-compliant tools for the public MCP ecosystem
Optimizing existing MCP servers for higher accuracy and lower token usage
Building custom bridges between internal enterprise APIs and Claude

About

Key Features

Dual-language implementation guides for Python and TypeScript SDKs
Automated schema validation strategies using Pydantic and Zod
Agent-centric tool design principles for optimized context usage
0 GitHub stars
Standardized error handling patterns for actionable agent feedback
Built-in evaluation framework for testing tool performance

Use Cases

Developing standard-compliant tools for the public MCP ecosystem
Optimizing existing MCP servers for higher accuracy and lower token usage
Building custom bridges between internal enterprise APIs and Claude