Can I use this skill to build read-only and destructive tools?

Yes, it provides guidance on tool annotations like readOnlyHint and destructiveHint to help the LLM understand the nature and safety of the tools it is using.

What is the primary purpose of the MCP Builder skill?

It provides a standardized, four-phase guide for creating high-quality Model Context Protocol (MCP) servers that allow LLMs to interact with external services via well-designed tools.

How does this skill help with testing AI tools?

It includes a dedicated evaluation phase that guides you through creating complex, realistic QA pairs in XML format to verify that the LLM can successfully use your server to solve tasks.

What is 'Agent-Centric Design' in the context of MCP?

It is a design philosophy that focuses on building tools for LLM workflows rather than just wrapping API endpoints, optimizing for limited context windows and providing actionable error messages.

Which programming frameworks are supported by this skill?

The skill includes detailed implementation patterns for both Python (FastMCP) and Node/TypeScript (official MCP SDK).

MCP Server Builder

Name: MCP Server Builder
Author: ev3rlit

byev3rlit

API開発

Architects high-quality Model Context Protocol (MCP) servers for seamless integration between LLMs and external services.

概要

The mcp-builder skill provides a comprehensive, four-phase framework for developing robust Model Context Protocol (MCP) servers using Python or TypeScript. It guides developers through agent-centric design principles, systematic implementation patterns, and rigorous evaluation strategies to ensure that AI agents can effectively navigate external APIs. By focusing on workflow-centric tool design and context optimization, this skill helps developers build integrations that maximize LLM utility while minimizing token overhead and error rates.

主な機能

Systematic implementation guides for shared utilities and input validation
0 GitHub stars
Comprehensive workflows for Python (FastMCP) and Node/TypeScript implementations
Evaluation-driven development framework for testing tool-calling effectiveness
Standardized error handling and response formatting for AI readability
Agent-centric design patterns to optimize tools for LLM context windows

ユースケース

Creating robust evaluation suites to measure LLM performance on complex task workflows
Building an MCP server to expose proprietary internal databases to Claude
Developing custom toolsets for interacting with third-party SaaS API endpoints

概要

主な機能

Systematic implementation guides for shared utilities and input validation
0 GitHub stars
Comprehensive workflows for Python (FastMCP) and Node/TypeScript implementations
Evaluation-driven development framework for testing tool-calling effectiveness
Standardized error handling and response formatting for AI readability
Agent-centric design patterns to optimize tools for LLM context windows

ユースケース

Creating robust evaluation suites to measure LLM performance on complex task workflows
Building an MCP server to expose proprietary internal databases to Claude
Developing custom toolsets for interacting with third-party SaaS API endpoints