Does this skill support both Python and TypeScript?

Yes, it provides detailed implementation guides and best practices for both the Python (FastMCP) and TypeScript SDKs.

What is the purpose of the evaluation phase?

The evaluation phase is a structured process to create complex, realistic test questions that verify whether an LLM can successfully use your MCP server to solve real-world problems accurately.

Can I use this for local development?

Yes, the guide includes instructions for local development using the stdio transport and testing with the MCP Inspector.

What is an MCP server?

An MCP (Model Context Protocol) server is an open standard that enables Large Language Models to interact with external data sources and tools through a consistent interface.

How does this help with tool discovery?

It provides guidance on descriptive naming conventions and clear parameter schemas to ensure AI agents can easily identify and utilize the correct tools for specific tasks.

MCP Server Builder

Name: MCP Server Builder
Author: majiayu000

bymajiayu000

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API開発

Guides the creation of high-quality Model Context Protocol (MCP) servers for integrating external services with LLMs.

概要

This skill provides a comprehensive framework for architecting, implementing, and testing Model Context Protocol (MCP) servers. It offers domain-specific guidance on tool naming, context management, and error handling, supporting both TypeScript and Python environments. Whether you are building local tools via stdio or remote services via streamable HTTP, this skill ensures your integrations are robust, discoverable, and optimized for agentic workflows through standardized implementation patterns and evaluation strategies.

主な機能

Integrated evaluation framework for testing tool effectiveness
Guided workflow for tool discovery and descriptive naming
1 GitHub stars
Support for both stdio and streamable HTTP transport mechanisms
Standardized MCP architecture patterns for TypeScript and Python
Comprehensive documentation for SDK integration and best practices

ユースケース

Building custom database connectors for LLM-driven data exploration
Connecting internal company APIs to AI agents for automated workflows
Developing standardized toolsets for third-party SaaS integrations

概要

主な機能

Integrated evaluation framework for testing tool effectiveness
Guided workflow for tool discovery and descriptive naming
1 GitHub stars
Support for both stdio and streamable HTTP transport mechanisms
Standardized MCP architecture patterns for TypeScript and Python
Comprehensive documentation for SDK integration and best practices

ユースケース

Building custom database connectors for LLM-driven data exploration
Connecting internal company APIs to AI agents for automated workflows
Developing standardized toolsets for third-party SaaS integrations