Mcp Builder

This is a comprehensive guide for building MCP servers that let LLMs interact with external APIs and services. It walks you through a four phase process starting with deep research into agent centric design principles (like building for workflows instead of just wrapping endpoints), studying the MCP protocol docs, then implementing tools systematically in either Python with FastMCP or TypeScript with the MCP SDK. The standout bit is the emphasis on designing for limited context windows and writing error messages that actually help agents figure out what to do next. It includes specific best practices for input validation with Pydantic or Zod, response formatting, and pagination handling.

skills.sh installs: 315
GitHub stars: 2.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add mrgoonie/claudekit-skills --skill mcp-builder --agent claude-code

Installs into .claude/skills of the current project.

Files

MCP Server Development Guide

Overview

To create high-quality MCP (Model Context Protocol) servers that enable LLMs to effectively interact with external services, use this skill. An MCP server provides tools that allow LLMs to access external services and APIs. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks using the tools provided.

Process

🚀 High-Level Workflow

Creating a high-quality MCP server involves four main phases:

Phase 1: Deep Research and Planning

1.1 Understand Agent-Centric Design Principles

Before diving into implementation, understand how to design tools for AI agents by reviewing these principles:

Build for Workflows, Not Just API Endpoints:

Don't simply wrap existing API endpoints - build thoughtful, high-impact workflow tools
Consolidate related operations (e.g., schedule_event that both checks availability and creates event)
Focus on tools that enable complete tasks, not just individual API calls
Consider what workflows agents actually need to accomplish

SKILL.md on GitHub