# MCP Builder

Guide for building high-quality MCP (Model Context Protocol) servers and tools: what...

- Repository: https://github.com/shareAI-lab/Kode-CLI (5235 stars)
- Category: Design
- License: Complete terms in LICENSE.txt
- Last updated: 2026-10-29
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add shareAI-lab/Kode-CLI --skill mcp-builder -g -a claude-code -y`
Codex: `npx skills add shareAI-lab/Kode-CLI --skill mcp-builder -g -a codex -y`
Cursor: `npx skills add shareAI-lab/Kode-CLI --skill mcp-builder -g -a cursor -y`
Gemini CLI: `npx skills add shareAI-lab/Kode-CLI --skill mcp-builder -g -a gemini-cli -y`

## Description

Guide for building high-quality MCP (Model Context Protocol) servers and tools: what MCP is, how to design tools/resources/prompts for agent usability, and how to implement servers in TypeScript or Python. Use when you need MCP best practices, server architecture patterns, or a step-by-step build workflow (with language preference).

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# MCP Server Development Guide

## Overview

Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks.

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# Process

## 🚀 High-Level Workflow

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

Before starting, confirm two preferences (ask once):
- Target implementation language/SDK (TypeScript SDK, Python SDK, or another official MCP SDK)
- Preferred language for the build walkthrough (English / 中文)

### Phase 1: Deep Research and Planning

#### 1.1 Understand Modern MCP Design

**API Coverage vs. Workflow Tools:**
Balance comprehensive API endpoint coverage with specialized workflow tools. Workflow tools can be more convenient for specific tasks, while comprehensive coverage gives agents flexibility to compose operations. Performance varies by client, some clients benefit from code execution that combines basic tools, while others work better with higher-level workflows. When uncertain, prioritize comprehensive API coverage.

**Tool Naming and Discoverability:**
Clear, descriptive tool names help agents find the right tools quickly. Use consistent prefixes (e.g., `github_create_issue`, `github_list_repos`) and action-oriented naming.

**Context Management:**
Agents benefit from concise tool descriptions and the ability to filter/paginate results. Design tools that return focused, relevant data. Some clients support code execution which can help agents filter and process data efficiently.

**Actionable Error Messages:**
Error messages should guide agents toward solutions with specific suggestions and next steps.

#### 1.2 Study MCP Protocol Documentation

**Navigate the MCP specification:**

Start with the sitemap to find relevant pages: `https://modelcontextprotocol.io/sitemap.xml`

The rest: https://github.com/shareAI-lab/Kode-CLI/blob/HEAD/packages/builtin-skills/skills/mcp-builder/SKILL.md