Does this skill support both Python and TypeScript?

Yes, it provides specific implementation guides for Python (FastMCP) and Node/TypeScript SDKs, including project structure and validation patterns.

How does this skill help optimize LLM context usage?

It teaches agent-centric design, focusing on returning high-signal information and implementing configurable response levels (Concise vs. Detailed) to save tokens.

What is included in the implementation plan phase?

The implementation plan covers tool selection, shared utilities, input/output schema design using Pydantic or Zod, and robust error handling strategies.

Can I use this to test my custom tools?

Yes, the skill includes a comprehensive phase for creating an evaluation harness with realistic QA pairs to verify your tool's effectiveness before deployment.

What is an MCP server?

An MCP (Model Context Protocol) server is a standard that enables AI models like Claude to interact with external tools, local files, and APIs through a unified interface.

MCP Server Builder

Name: MCP Server Builder
Author: Microck

byMicrock

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API 개발

Guides developers through the end-to-end creation of high-quality Model Context Protocol (MCP) servers for LLM tool integration.

소개

The MCP Server Builder skill empowers developers to architect and implement robust integrations between LLMs and external services. It moves beyond simple API wrapping by teaching agent-centric design principles, ensuring tools are optimized for context windows and complex workflows. Whether you are using Python (FastMCP) or Node/TypeScript, this skill provides a structured framework for deep research, implementation with Pydantic or Zod validation, and the creation of rigorous evaluation harnesses to verify that your tools perform effectively in real-world scenarios.

주요 기능

Agent-centric design patterns for workflow-optimized tools
Comprehensive error handling and natural language feedback design
81 GitHub stars
Support for both Python (FastMCP) and Node/TypeScript SDKs
Advanced response formatting (JSON/Markdown) and truncation strategies
Evaluation-driven development for verifying tool performance

사용 사례

Connecting Claude to proprietary internal APIs and databases
Building high-signal tools for public service integrations
Refining existing MCP servers to improve AI context efficiency

소개

주요 기능

Agent-centric design patterns for workflow-optimized tools
Comprehensive error handling and natural language feedback design
81 GitHub stars
Support for both Python (FastMCP) and Node/TypeScript SDKs
Advanced response formatting (JSON/Markdown) and truncation strategies
Evaluation-driven development for verifying tool performance

사용 사례

Connecting Claude to proprietary internal APIs and databases
Building high-signal tools for public service integrations
Refining existing MCP servers to improve AI context efficiency