What makes a 'high-quality' MCP tool?

High-quality tools focus on complete human-centric workflows rather than raw API endpoints, providing actionable error messages and strict input validation.

What is an MCP server?

An MCP (Model Context Protocol) server provides tools that allow LLMs to interact with external services and data through a standardized communication protocol.

How should I test my MCP server safely?

The skill recommends using an evaluation harness or running the server in a separate process like tmux to avoid hanging the main development environment.

How does this help with AI context limits?

It provides specific design principles for returning high-signal, concise information and managing token budgets to prevent context window overflow.

Which languages are supported by this skill?

This skill provides implementation guides and best practices for both Python (using FastMCP/SDK) and Node/TypeScript (using the official MCP SDK).

MCP Server Builder

Name: MCP Server Builder
Author: Microck

byMicrock

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API Development

Builds high-quality Model Context Protocol (MCP) servers to integrate external APIs and services with AI agents.

About

The MCP Builder skill provides a comprehensive framework for developing robust Model Context Protocol (MCP) servers in Python and TypeScript. It guides developers through the entire lifecycle of server creation—from agent-centric design principles and workflow planning to implementation and rigorous evaluation. By focusing on limited context optimization, actionable error messages, and task-driven tool design, this skill ensures that external services are seamlessly and effectively accessible to LLMs like Claude.

Key Features

Support for both Python (FastMCP) and Node/TypeScript SDKs
Agent-centric tool design principles for optimized AI workflows
Comprehensive evaluation framework for testing server effectiveness
Structured research guidelines for API integration and protocol specs
Implementation patterns for input validation and error handling
81 GitHub stars

Use Cases

Building standardized API bridges for external SaaS platforms
Developing highly-testable tool integrations for specialized AI agents
Creating custom MCP servers to connect proprietary internal tools with Claude

About

Key Features

Support for both Python (FastMCP) and Node/TypeScript SDKs
Agent-centric tool design principles for optimized AI workflows
Comprehensive evaluation framework for testing server effectiveness
Structured research guidelines for API integration and protocol specs
Implementation patterns for input validation and error handling
81 GitHub stars

Use Cases

Building standardized API bridges for external SaaS platforms
Developing highly-testable tool integrations for specialized AI agents
Creating custom MCP servers to connect proprietary internal tools with Claude