What is an MCP server?

An MCP (Model Context Protocol) server is a specialized application that allows Large Language Models to interact with external data and services through a standardized interface.

How does this skill help with tool naming?

The skill provides guidance on using action-oriented naming and consistent prefixes to ensure AI agents can easily discover and select the correct tool for a given task.

What is the recommended way to test an MCP server?

The guide recommends using the MCP Inspector for local testing and creating a structured evaluation suite with 10 complex questions to verify LLM performance.

Should I use TypeScript or Python for my MCP server?

Both are supported. TypeScript is recommended for its high-quality SDK and compatibility with many execution environments, while Python (FastMCP) is excellent for rapid development and data-heavy applications.

What transport mechanisms does the mcp-builder suggest?

It recommends Streamable HTTP for remote servers due to its scalability, and stdio for local servers used within a desktop environment.

MCP Server Builder

Name: MCP Server Builder
Author: partme-ai

bypartme-ai

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API Development

Guides the end-to-end development of high-quality Model Context Protocol (MCP) servers for seamless LLM integration.

About

The mcp-builder skill provides a structured framework for developers to create, test, and evaluate MCP servers that enable Claude and other LLMs to interact with external services. It offers deep domain expertise in designing well-structured tools, selecting appropriate transport mechanisms (stdio vs. HTTP), and implementing robust error handling. Whether using TypeScript with the official SDK or Python with FastMCP, this skill ensures your tools provide the right context and discoverability for AI agents to solve complex, real-world tasks effectively.

Key Features

Tailored guidance for TypeScript (MCP SDK) and Python (FastMCP) development
Protocol-specific documentation retrieval for the latest MCP standards
37 GitHub stars
Comprehensive 4-phase workflow covering research, implementation, testing, and evaluation
Best practices for tool naming, schema definition using Zod/Pydantic, and context management
Rigorous evaluation methodology for verifying LLM tool-calling reliability

Use Cases

Creating robust test suites to ensure AI agents can handle complex, multi-step tool interactions
Building custom connectors to internal APIs to allow Claude to access proprietary data
Developing specialized workflow tools that combine multiple API operations into single agent actions

About

Key Features

Tailored guidance for TypeScript (MCP SDK) and Python (FastMCP) development
Protocol-specific documentation retrieval for the latest MCP standards
37 GitHub stars
Comprehensive 4-phase workflow covering research, implementation, testing, and evaluation
Best practices for tool naming, schema definition using Zod/Pydantic, and context management
Rigorous evaluation methodology for verifying LLM tool-calling reliability

Use Cases

Creating robust test suites to ensure AI agents can handle complex, multi-step tool interactions
Building custom connectors to internal APIs to allow Claude to access proprietary data
Developing specialized workflow tools that combine multiple API operations into single agent actions