What is an MCP server?

An MCP (Model Context Protocol) server is a bridge that allows LLMs to securely interact with external services, APIs, and local data through standardized tool sets.

What are 'Agent-Centric Design Principles'?

These are design rules that prioritize building tools for workflows rather than just wrapping API endpoints, ensuring error messages guide the agent toward correct usage.

Can I use this to test my custom tools?

Absolutely. It includes a specific phase for evaluation-driven development, helping you create realistic test scenarios to verify your server's accuracy.

How does this skill improve tool performance for AI?

It implements agent-centric design principles, focusing on workflow-based tools and concise data returns that respect the agent's limited context window.

Does this skill support both Python and JavaScript?

Yes, it provides dedicated implementation paths for both Python (using FastMCP and Pydantic) and Node.js/TypeScript (using the MCP SDK and Zod).

MCP Server Builder

Name: MCP Server Builder
Author: yusoofsh

byyusoofsh

API 开发

Streamlines the development of high-quality Model Context Protocol (MCP) servers to connect AI agents with external APIs and services.

关于

mcp-builder provides a comprehensive framework for developing Model Context Protocol (MCP) servers in both Python and Node/TypeScript environments. It emphasizes agent-centric design, guiding developers to build tools optimized for LLM context windows with robust error handling and logical task workflows. By following its structured phases—from research and implementation to evaluation-driven development—you can ensure your AI tools are reliable, high-performing, and easy for Claude to navigate when interacting with external data sources.

主要功能

Comprehensive implementation guides for Python (FastMCP) and Node/TypeScript SDKs
Agent-centric design principles for optimized LLM tool-calling performance
Standardized patterns for input validation using Pydantic and Zod
Built-in evaluation framework for creating and verifying tool-use scenarios
Best practices for handling pagination, character limits, and actionable error messages
0 GitHub stars

使用场景

Building specialized data retrieval tools that optimize LLM context window usage
Developing custom SaaS integrations for platforms like Slack, GitHub, or Jira
Integrating internal company databases or proprietary APIs with Claude

关于

主要功能

Comprehensive implementation guides for Python (FastMCP) and Node/TypeScript SDKs
Agent-centric design principles for optimized LLM tool-calling performance
Standardized patterns for input validation using Pydantic and Zod
Built-in evaluation framework for creating and verifying tool-use scenarios
Best practices for handling pagination, character limits, and actionable error messages
0 GitHub stars

使用场景

Building specialized data retrieval tools that optimize LLM context window usage
Developing custom SaaS integrations for platforms like Slack, GitHub, or Jira
Integrating internal company databases or proprietary APIs with Claude