What is an MCP server?

An MCP (Model Context Protocol) server is a specialized service that provides tools, resources, and prompts to Large Language Models, allowing them to interact securely with external data and APIs.

How does this skill help with token management?

It includes specific guidelines for 'Limited Context' design, teaching you how to return high-signal information and implement truncation strategies to stay within LLM context windows.

Does this skill support Python and TypeScript?

Yes, it provides detailed implementation guides, library recommendations (FastMCP and MCP SDK), and code patterns for both Python and Node/TypeScript environments.

How do I test my MCP server with this skill?

The skill includes a dedicated Evaluation Phase that guides you through creating 10 realistic, verifiable QA pairs in an XML format to benchmark your server's effectiveness.

MCP Server Builder

Name: MCP Server Builder
Author: Krosebrook

byKrosebrook

•

API 开发

Guides developers through the end-to-end process of building high-quality Model Context Protocol (MCP) servers for AI agents.

关于

The MCP Server Builder skill provides a comprehensive framework for architecting, implementing, and evaluating Model Context Protocol (MCP) servers. It emphasizes agent-centric design principles, ensuring that tools are optimized for LLM context windows and real-world workflows rather than just wrapping raw API endpoints. By providing standardized patterns for both Python (FastMCP) and Node/TypeScript implementations, it enables developers to create robust bridges between AI models and external services with proper error handling, input validation, and systematic evaluation suites.

主要功能

Support for both Python (FastMCP) and Node/TypeScript SDK architectures
Agent-centric design patterns for optimizing LLM context usage
1 GitHub stars
Automated evaluation framework for verifying tool performance with XML-based Q&A
Step-by-step workflow from deep research to implementation and testing
Standardized input validation templates using Pydantic and Zod

使用场景

Optimizing existing AI tools to reduce token usage and improve error recovery
Building custom toolsets to connect internal proprietary APIs with Claude
Converting legacy REST/GraphQL services into standardized MCP servers

关于

主要功能

Support for both Python (FastMCP) and Node/TypeScript SDK architectures
Agent-centric design patterns for optimizing LLM context usage
1 GitHub stars
Automated evaluation framework for verifying tool performance with XML-based Q&A
Step-by-step workflow from deep research to implementation and testing
Standardized input validation templates using Pydantic and Zod

使用场景

Optimizing existing AI tools to reduce token usage and improve error recovery
Building custom toolsets to connect internal proprietary APIs with Claude
Converting legacy REST/GraphQL services into standardized MCP servers