What is agent-centric design in the context of MCP?

It is the practice of designing tools specifically for AI agents, focusing on high-signal information, consolidated workflows, and actionable error messages rather than raw API endpoints.

Why is tool documentation important for MCP servers?

Detailed docstrings and descriptions act as the 'instruction manual' for the LLM, explaining when to use a tool, what parameters mean, and how to interpret responses.

How does this skill help with testing MCP servers?

It provides a dedicated phase for creating an evaluation harness with 10 complex, realistic QA pairs to verify that the LLM can successfully navigate the server's tools.

Does this skill support both Python and TypeScript?

Yes, it includes specific implementation guides for the Python FastMCP framework and the official Node/TypeScript MCP SDK.

What is the primary goal of the mcp-builder skill?

It provides a structured workflow to build high-quality MCP servers that enable LLMs to interact with external services through well-designed, workflow-oriented tools.

MCP Server Builder

Name: MCP Server Builder
Author: frostant

byfrostant

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API Development

Guides the creation of high-quality Model Context Protocol (MCP) servers to bridge LLMs with external services and APIs.

About

The mcp-builder skill provides a standardized, phased framework for developing robust Model Context Protocol (MCP) servers in both Python and Node/TypeScript. It emphasizes agent-centric design principles, ensuring that tools are optimized for AI context windows and complex workflows rather than simple API wrapping. By following this guided process—from deep research and implementation to rigorous evaluation—developers can build reliable integrations that allow Claude and other LLMs to interact seamlessly with private data, external APIs, and custom software environments.

Key Features

Comprehensive implementation planning for input validation, pagination, and error handling
Multi-language support for Python (FastMCP) and Node/TypeScript SDK implementations
Standardized tool annotation patterns including readOnly and idempotent hints
Agent-centric design guidelines to optimize tools for limited AI context windows
1 GitHub stars
Integrated evaluation framework for testing tool effectiveness through realistic QA scenarios

Use Cases

Developing a custom MCP server to connect proprietary enterprise APIs to Claude
Creating automated evaluation harnesses to verify MCP tool behavior across complex tasks
Optimizing existing developer tools for better AI agent performance and reliability

About

Key Features

Comprehensive implementation planning for input validation, pagination, and error handling
Multi-language support for Python (FastMCP) and Node/TypeScript SDK implementations
Standardized tool annotation patterns including readOnly and idempotent hints
Agent-centric design guidelines to optimize tools for limited AI context windows
1 GitHub stars
Integrated evaluation framework for testing tool effectiveness through realistic QA scenarios

Use Cases

Developing a custom MCP server to connect proprietary enterprise APIs to Claude
Creating automated evaluation harnesses to verify MCP tool behavior across complex tasks
Optimizing existing developer tools for better AI agent performance and reliability