Can I use this to test my MCP server?

Yes, the skill includes a dedicated phase for creating evaluation questions and harnesses to verify tool reliability and accuracy before deployment.

Does this skill support both Python and Node.js?

Yes, it provides specific implementation guides, best practices, and SDK references for both Python (FastMCP) and Node/TypeScript environments.

How does this help with agent performance?

It provides design principles focused on building workflow-centric tools rather than simple API wrappers, which helps agents complete complex tasks with fewer errors.

What is an MCP server?

An MCP (Model Context Protocol) server is a bridge that allows LLMs like Claude to securely interact with external tools, APIs, and data sources through a standardized protocol.

What is agent-centric design?

Agent-centric design is the practice of creating tools specifically for the constraints of AI agents, focusing on token efficiency, clear descriptions, and actionable error messages.

MCP Builder

Name: MCP Builder
Author: prof-ramos

byprof-ramos

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API Development

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

About

The mcp-builder skill provides a comprehensive framework for developing MCP servers that allow LLMs to interact with external APIs and services. It emphasizes agent-centric design principles, such as workflow optimization and actionable error messages, while providing language-specific guidance for both Python (FastMCP) and Node/TypeScript. From initial research and API study to implementation and creating rigorous evaluation harnesses, this skill ensures that MCP tools are robust, efficient, and highly effective for real-world AI agent tasks.

Key Features

Guidance on handling context limits and token management
Best practices for error handling and input validation using Pydantic or Zod
Automated evaluation generation for testing tool effectiveness
1 GitHub stars
Step-by-step workflow for Python and TypeScript MCP development
Agent-centric design principles for optimized tool performance

Use Cases

Standardizing MCP development across a team using shared utility patterns
Building custom integrations between Claude and internal business APIs
Optimizing existing MCP servers for better agent reasoning and task completion

About

Key Features

Guidance on handling context limits and token management
Best practices for error handling and input validation using Pydantic or Zod
Automated evaluation generation for testing tool effectiveness
1 GitHub stars
Step-by-step workflow for Python and TypeScript MCP development
Agent-centric design principles for optimized tool performance

Use Cases

Standardizing MCP development across a team using shared utility patterns
Building custom integrations between Claude and internal business APIs
Optimizing existing MCP servers for better agent reasoning and task completion