ak-mcp-builder

name	ak:mcp-builder
description	Build MCP servers for LLM-external service integration. Use for FastMCP (Python), MCP SDK (Node/TypeScript), tool design, API integration, resource providers.
user-invocable	true
when_to_use	Invoke when building an MCP server or tool surface.
category	dev-tools
keywords	["MCP","server","tools","integration"]
license	Complete terms in LICENSE.txt
argument-hint	[service or API to integrate]
metadata	{"author":"agentkit","version":"1.0.0"}

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.

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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 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

Optimize for Limited Context:

• Agents have constrained context windows - make every token count
• Return high-signal information, not exhaustive data dumps
• Provide "concise" vs "detailed" response format options
• Default to human-readable identifiers over technical codes (names over IDs)
• Consider the agent's context budget as a scarce resource

Design Actionable Error Messages:

• Error messages should guide agents toward correct usage patterns
• Suggest specific next steps: "Try using filter='active_only' to reduce results"
• Make errors educational, not just diagnostic
• Help agents learn proper tool usage through clear feedback

Follow Natural Task Subdivisions:

• Tool names should reflect how humans think about tasks
• Group related tools with consistent prefixes for discoverability
• Design tools around natural workflows, not just API structure

Use Evaluation-Driven Development:

• Create realistic evaluation scenarios early
• Let agent feedback drive tool improvements
• Prototype quickly and iterate based on actual agent performance

1.3 Study MCP Protocol Documentation

Fetch the latest MCP protocol documentation:

Use web_search capability to load: