How do I test an MCP server built with this skill?

The skill includes a dedicated phase for creating evaluation questions and XML-based QA pairs to verify that the LLM can successfully solve complex, real-world tasks using your tools.

Which programming languages does this skill support?

It provides detailed implementation guides, SDK references, and best practices for both Python (using FastMCP) and Node/TypeScript.

What is the primary goal of the MCP Builder skill?

The primary goal is to guide developers in creating high-quality MCP servers that allow LLMs to interact effectively with external services through well-designed, workflow-oriented tools.

What is 'Agent-Centric Design' in the context of MCP?

It is a design philosophy focused on building tools for workflows rather than just endpoints, optimizing data for limited context windows, and providing error messages that help agents self-correct.

MCP Server Builder

Name: MCP Server Builder
Author: rafaelcalleja

byrafaelcalleja

•

API Development

Guides the creation of high-quality Model Context Protocol (MCP) servers to enable seamless LLM interaction with external APIs and services.

About

The mcp-builder skill provides a comprehensive, end-to-end framework for developing robust MCP servers in both Python and TypeScript. It moves beyond simple API wrapping by emphasizing agent-centric design principles, such as workflow-based tool architecture, context window optimization, and actionable error handling. Developers can follow a structured four-phase process—encompassing deep research, systematic implementation, code refinement, and automated evaluation—to ensure their integrations are efficient, reliable, and highly performant for AI agents like Claude.

Key Features

Context window management and token-efficient response formatting
Dual-language support for Python (FastMCP) and Node/TypeScript SDKs
Comprehensive implementation checklists and code quality reviews
Built-in evaluation framework for validating tool accuracy and stability
Agent-centric design principles for optimized tool workflows
1 GitHub stars

Use Cases

Refactoring existing service integrations to be more 'agent-friendly'
Building custom API connectors to bridge internal databases with AI agents
Creating automated test suites to evaluate tool performance in real-world scenarios

About

Key Features

Context window management and token-efficient response formatting
Dual-language support for Python (FastMCP) and Node/TypeScript SDKs
Comprehensive implementation checklists and code quality reviews
Built-in evaluation framework for validating tool accuracy and stability
Agent-centric design principles for optimized tool workflows
1 GitHub stars

Use Cases

Refactoring existing service integrations to be more 'agent-friendly'
Building custom API connectors to bridge internal databases with AI agents
Creating automated test suites to evaluate tool performance in real-world scenarios