What is an MCP server?

An MCP (Model Context Protocol) server is a standard that allows AI models like Claude to securely connect with local or remote data sources and tools.

How do I test my MCP server?

The skill includes a dedicated phase for evaluation-driven development, guiding you through creating realistic Q&A pairs to verify tool effectiveness.

Can I use this for production integrations?

Absolutely. It follows best practices for error handling, input validation, and character limits required for production-grade AI tools.

How does this help with agent performance?

It emphasizes agent-centric design, focusing on workflow-based tools and optimized context usage rather than just wrapping API endpoints.

Does this skill support Python and TypeScript?

Yes, the mcp-builder skill provides detailed implementation guides for both the Python (FastMCP) and Node/TypeScript MCP SDKs.

MCP Server Builder

Name: MCP Server Builder
Author: ComposioHQ

byComposioHQ

•

7,895

Desarrollo de API

Builds high-performance Model Context Protocol (MCP) servers to integrate external APIs and services with Claude.

Acerca de

MCP Server Builder provides a comprehensive framework and standardized workflow for developing Model Context Protocol (MCP) servers. It guides developers through the entire lifecycle of server creation—from initial research and agent-centric design principles to implementation in Python (FastMCP) or TypeScript. By focusing on workflow-oriented tools rather than simple API wrapping, this skill helps create robust integrations that allow Claude to interact seamlessly with external data sources, perform complex tasks, and handle errors gracefully using industry best practices.

Características Principales

Step-by-step guidance for Python (FastMCP) and Node/TypeScript SDKs
7,895 GitHub stars
Evaluation-driven development with automated testing workflows
Agent-centric design principles for optimized LLM tool usage
Best practices for context management and token optimization
Comprehensive error handling and natural language feedback strategies

Casos de Uso

Building high-quality MCP servers for public SaaS platforms
Optimizing existing MCP servers for better agent performance and reliability
Integrating custom internal company APIs with Claude AI

Acerca de

Características Principales

Step-by-step guidance for Python (FastMCP) and Node/TypeScript SDKs
7,895 GitHub stars
Evaluation-driven development with automated testing workflows
Agent-centric design principles for optimized LLM tool usage
Best practices for context management and token optimization
Comprehensive error handling and natural language feedback strategies

Casos de Uso

Building high-quality MCP servers for public SaaS platforms
Optimizing existing MCP servers for better agent performance and reliability
Integrating custom internal company APIs with Claude AI