How does this help with AI tool discoverability?

It provides standardized naming conventions and description patterns that ensure LLMs can easily identify and select the correct tool for a specific task.

Can I use this to test my MCP implementation?

Yes, the skill includes a dedicated evaluation phase with instructions for using the MCP Inspector and creating realistic QA pairs to verify server performance.

What is an MCP server?

A Model Context Protocol (MCP) server is a specialized bridge that allows LLMs to interact securely and efficiently with external data sources, services, and local tools.

What transport mechanisms are covered?

The guide covers both stdio for local server integrations and streamable HTTP for remote server deployments.

Does this skill support both Python and Node.js?

Yes, it provides specific implementation patterns and guides for both Python (via FastMCP) and Node.js/TypeScript (via the official MCP SDK).

MCP Server Builder

Name: MCP Server Builder
Author: unclecatvn

byunclecatvn

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API Development

Guides the creation of robust Model Context Protocol (MCP) servers for seamless LLM-to-API integration.

About

The mcp-builder skill provides a structured framework for developing high-quality Model Context Protocol (MCP) servers in TypeScript and Python. It offers comprehensive guidance on tool design, authentication, pagination, and error handling, ensuring that LLMs can effectively interact with external services through well-defined schemas. This skill is essential for developers looking to extend AI capabilities by building custom tools that connect agents to real-world APIs and data sources using best-practice implementation patterns.

Key Features

Structured evaluation framework to verify LLM tool effectiveness
1 GitHub stars
Comprehensive guidance for authentication, pagination, and rate limiting
Standardized tool naming and discoverability best practices
Multi-language support for TypeScript (MCP SDK) and Python (FastMCP)
Deep integration with the official Model Context Protocol specification

Use Cases

Building custom MCP bridges for proprietary internal APIs
Creating automated evaluation sets to verify AI tool-calling accuracy
Optimizing existing MCP servers for better agent discoverability and performance

About

Key Features

Structured evaluation framework to verify LLM tool effectiveness
1 GitHub stars
Comprehensive guidance for authentication, pagination, and rate limiting
Standardized tool naming and discoverability best practices
Multi-language support for TypeScript (MCP SDK) and Python (FastMCP)
Deep integration with the official Model Context Protocol specification

Use Cases

Building custom MCP bridges for proprietary internal APIs
Creating automated evaluation sets to verify AI tool-calling accuracy
Optimizing existing MCP servers for better agent discoverability and performance