How does agent-centric design differ from standard API design?

Agent-centric design focuses on high-signal data, concise context-aware responses, and actionable error messages that help an AI model self-correct.

Does this skill support both Python and TypeScript?

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

What is an MCP server?

A Model Context Protocol (MCP) server is a standardized bridge that enables Large Language Models to securely access external data, services, and tools.

Can I use this to wrap existing REST APIs?

Absolutely. It is designed specifically to help you turn existing API documentation into high-quality tools that an AI agent can effectively use.

How do I test the MCP servers I build with this skill?

The skill includes a dedicated evaluation phase where you create realistic Q&A scenarios to verify the LLM can successfully navigate your server's tools.

MCP Server Builder

Name: MCP Server Builder
Author: pur3v4d3r

bypur3v4d3r

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API 开发

Builds high-quality Model Context Protocol (MCP) servers to enable LLMs to interact with external services and APIs through well-designed tools.

关于

The mcp-builder skill provides a comprehensive, four-phase framework for developing robust Model Context Protocol servers in Python or TypeScript. It guides developers through agent-centric design principles, ensuring tools are optimized for workflow automation rather than just simple API wrapping. By integrating protocol documentation, SDK best practices, and evaluation-driven development, this skill helps create production-grade integrations that respect AI context windows and provide actionable feedback to autonomous agents.

主要功能

Agent-centric tool design principles for workflow optimization
Automated fetching of latest MCP protocol and SDK documentation
Standardized error handling and response formatting patterns
1 GitHub stars
Comprehensive implementation guides for Python (FastMCP) and Node/TypeScript
Built-in evaluation framework for testing tool effectiveness

使用场景

Designing complex workflow-based tools that consolidate multiple API operations into single agent actions
Developing custom API connectors for Claude to interact with internal business tools
Building robust evaluation harnesses to verify the accuracy and reliability of LLM tool usage

关于

主要功能

Agent-centric tool design principles for workflow optimization
Automated fetching of latest MCP protocol and SDK documentation
Standardized error handling and response formatting patterns
1 GitHub stars
Comprehensive implementation guides for Python (FastMCP) and Node/TypeScript
Built-in evaluation framework for testing tool effectiveness

使用场景

Designing complex workflow-based tools that consolidate multiple API operations into single agent actions
Developing custom API connectors for Claude to interact with internal business tools
Building robust evaluation harnesses to verify the accuracy and reliability of LLM tool usage