What are agent-centric design principles?

It refers to designing tools around complete workflows and tasks rather than just wrapping API endpoints, making it easier for AI agents to solve complex problems autonomously.

What is an MCP server?

An MCP (Model Context Protocol) server is a bridge that allows Large Language Models to interact with external services and data through a standardized protocol of tools and resources.

Does this skill support Python and TypeScript?

Yes, it provides specific implementation guides for both the Python (FastMCP) and Node.js/TypeScript MCP SDKs, including project structure and type-safe validation.

How does this skill optimize for LLM context windows?

It includes strategies for data truncation, providing concise vs. detailed response options, and prioritizing high-signal information to keep token usage within model limits.

MCP Server Builder

Name: MCP Server Builder
Author: organvm-iv-taxis

byorganvm-iv-taxis

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API Development

Guides the creation of high-quality Model Context Protocol (MCP) servers to bridge LLMs with external services using Python or TypeScript.

This skill provides a comprehensive framework for developing robust MCP servers, focusing on agent-centric design principles that optimize tool usage for AI models. It guides developers through the entire lifecycle—from deep research and API planning to implementation with Python or TypeScript SDKs and rigorous evaluation. By emphasizing concise context management and actionable error handling, it ensures that resulting MCP servers are highly effective for real-world autonomous agent workflows and complex tool-calling tasks.

Key Features

012 GitHub stars

02Best practices for API pagination and token-efficient data truncation

03Support for both Python (FastMCP) and Node/TypeScript SDKs

04Agent-centric tool design optimization for better LLM performance

05Context-aware error handling and response formatting patterns

06Framework for creating automated evaluation harnesses

Use Cases

01Integrating proprietary internal databases with Claude for data analysis

02Building custom toolsets for interacting with third-party SaaS APIs

03Standardizing AI agent workflows across complex engineering environments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add organvm-iv-taxis/a-i--skills mcp-builder

For use in Claude.ai and ChatGPT

Key Features

012 GitHub stars

02Best practices for API pagination and token-efficient data truncation

03Support for both Python (FastMCP) and Node/TypeScript SDKs

04Agent-centric tool design optimization for better LLM performance

05Context-aware error handling and response formatting patterns

06Framework for creating automated evaluation harnesses

Use Cases

01Integrating proprietary internal databases with Claude for data analysis

02Building custom toolsets for interacting with third-party SaaS APIs

03Standardizing AI agent workflows across complex engineering environments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add organvm-iv-taxis/a-i--skills mcp-builder

For use in Claude.ai and ChatGPT