Does this skill support both Python and TypeScript?

Yes, it provides detailed implementation guides, project structures, and best practices for both the Python FastMCP framework and the Node/TypeScript MCP SDK.

What is the Model Context Protocol (MCP)?

The Model Context Protocol is an open standard that enables LLMs to securely and efficiently interact with external data sources and tools through standardized server implementations.

How does this skill improve LLM tool usage?

It provides specific guidance on tool naming, descriptive field schemas, and response truncation strategies that ensure the LLM understands and uses tools correctly within its context window.

Can I use this to test my MCP server implementation?

Absolutely. The skill includes a dedicated phase for evaluation-driven development, including testing with the MCP Inspector and creating complex QA pairs for performance verification.

MCP Server Builder

Name: MCP Server Builder
Author: basher83

bybasher83

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API Development

Architects high-quality Model Context Protocol (MCP) servers to bridge the gap between LLMs and external service APIs.

About

The mcp-builder skill provides a comprehensive development framework for creating robust MCP servers in both Python and TypeScript. It guides developers through the entire lifecycle of server creation, from initial API research and workflow planning to implementation and rigorous evaluation. By focusing on LLM-centric design patterns—such as context window optimization, actionable error messaging, and structured tool discovery—this skill ensures that the resulting tools are highly discoverable and efficient for AI agents to use in real-world scenarios.

Key Features

Context management strategies to optimize token usage and agent efficiency
Comprehensive implementation patterns for Python (FastMCP) and Node/TypeScript SDKs
5 GitHub stars
Actionable error handling patterns designed specifically for LLM feedback loops
Standardized tool naming and discovery protocols for better model performance
Evaluation-driven development workflows with automated QA pair generation

Use Cases

Developing high-performance MCP servers for local data processing using stdio transport
Building custom API integrations that allow Claude to interact with internal business tools
Creating scalable remote servers using streamable HTTP and stateless JSON transport

About

Key Features

Context management strategies to optimize token usage and agent efficiency
Comprehensive implementation patterns for Python (FastMCP) and Node/TypeScript SDKs
5 GitHub stars
Actionable error handling patterns designed specifically for LLM feedback loops
Standardized tool naming and discovery protocols for better model performance
Evaluation-driven development workflows with automated QA pair generation

Use Cases

Developing high-performance MCP servers for local data processing using stdio transport
Building custom API integrations that allow Claude to interact with internal business tools
Creating scalable remote servers using streamable HTTP and stateless JSON transport