Does this skill help with input validation?

Yes, it provides guidance on using Pydantic for Python and Zod for TypeScript to ensure robust tool input validation and clear error messaging.

Which programming languages are supported by this skill?

The guide provides specific implementation workflows and best practices for both Python (using FastMCP) and Node/TypeScript (using the MCP SDK).

What is an MCP server?

An MCP (Model Context Protocol) server acts as a bridge that allows LLMs to interact with external services and APIs through standardized, well-designed tools.

Why focus on agent-centric design?

Agent-centric design ensures tools are optimized for an LLM's context window, providing high-signal data and actionable errors rather than raw API dumps that consume tokens.

How do I test my MCP server safely?

It is recommended to use an evaluation harness or run the server in a separate process (like tmux) to avoid hanging the main development process during testing.

MCP Server Builder

Name: MCP Server Builder
Author: billlzzz18

bybilllzzz18

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API 개발

Provides a comprehensive framework for designing, implementing, and evaluating high-quality Model Context Protocol (MCP) servers.

소개

The MCP Server Builder skill empowers developers to create robust Model Context Protocol (MCP) integrations that bridge LLMs with external APIs and services. It provides a structured four-phase workflow—Deep Research, Implementation, Review, and Evaluation—ensuring that tools are optimized for AI agent context windows, offer actionable error messages, and follow natural task subdivisions. Whether using Python's FastMCP or the Node/TypeScript SDK, this skill guides you through building agent-centric tools that handle complex real-world tasks effectively.

주요 기능

Best practices for context optimization and token management
Agent-centric design principles for optimal LLM interaction
Step-by-step implementation workflows for Python and TypeScript
Comprehensive evaluation framework with automated QA generation
1 GitHub stars
Guidance on input validation using Pydantic and Zod schemas

사용 사례

Creating robust evaluation datasets to test AI agent tool-calling performance
Building custom MCP servers to integrate internal company APIs with Claude
Designing high-signal workflow tools that minimize token usage in agentic loops

소개

주요 기능

Best practices for context optimization and token management
Agent-centric design principles for optimal LLM interaction
Step-by-step implementation workflows for Python and TypeScript
Comprehensive evaluation framework with automated QA generation
1 GitHub stars
Guidance on input validation using Pydantic and Zod schemas

사용 사례

Creating robust evaluation datasets to test AI agent tool-calling performance
Building custom MCP servers to integrate internal company APIs with Claude
Designing high-signal workflow tools that minimize token usage in agentic loops