# MCP Server Builder

Guide building MCP servers in Python or TypeScript that wrap external APIs.

- Repository: https://github.com/anthropics/skills (186321 stars)
- Category: AI and agents
- License: Complete terms in LICENSE.txt
- Last updated: 2026-10-28
- Runs scripts: yes, review before use

## Install

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

## Description

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

## 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 four main phases:

### 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`

Then fetch specific pages with `.md` suffix for markdown format (e.g., `https://modelcontextprotocol.io/specification/draft.md`).

Key pages to review:
- Specification overview and architecture
- Transport mechanisms (streamable HTTP, stdio)
- Tool, resource, and prompt definitions

The rest: https://github.com/anthropics/skills/blob/HEAD/skills/mcp-builder/SKILL.md