Why is the tool description more important than the code?

The LLM never sees your underlying implementation; it only sees the schema and description. Clear descriptions are essential for the agent to understand exactly when and how to call a tool correctly.

How does this skill help reduce token costs?

By optimizing schemas and providing crystal-clear instructions, it prevents the agent from making incorrect calls or getting stuck in loops, significantly reducing token waste.

Does this skill support the Model Context Protocol (MCP)?

Yes, it includes specialized patterns for the emerging MCP standard, which is becoming the industry lingua franca for connecting AI agents to external tools.

What common mistakes does the Agent Tool Builder help prevent?

It helps avoid key anti-patterns such as vague tool descriptions, silent failures that leave the agent confused, and providing too many tools at once, which can overwhelm the model's context.

Agent Tool Builder

Name: Agent Tool Builder
Author: claudiodearaujo

byclaudiodearaujo

0•

API Development

Standardizes the creation of high-performance AI agent tools through optimized schemas, descriptive documentation, and robust error handling.

The Agent Tool Builder skill empowers developers to bridge the gap between AI models and external environments by focusing on the critical interface layer. It provides expert guidance on crafting precise JSON schemas and crystal-clear descriptions that prevent agent hallucinations and silent failures. By incorporating emerging standards like the Model Context Protocol (MCP) and best practices for validation and error recovery, this skill ensures that AI agents interact with APIs efficiently, reliably, and at a lower token cost.

Key Features

01Advanced tool description writing to reduce LLM hallucinations

020 GitHub stars

03Input validation patterns and usage examples for agent guidance

04Strategic error handling to help agents recover from failures

05Implementation of the Model Context Protocol (MCP) standard

06Optimized JSON Schema design for unambiguous function calling

Use Cases

01Refactoring existing APIs for better compatibility with LLM function calling

02Building robust toolsets for custom AI agents and assistants

03Implementing standardized communication layers using the Model Context Protocol (MCP)

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/izacenter agent-tool-builder

For use in Claude.ai and ChatGPT

Key Features

01Advanced tool description writing to reduce LLM hallucinations

020 GitHub stars

03Input validation patterns and usage examples for agent guidance

04Strategic error handling to help agents recover from failures

05Implementation of the Model Context Protocol (MCP) standard

06Optimized JSON Schema design for unambiguous function calling

Use Cases

01Refactoring existing APIs for better compatibility with LLM function calling

02Building robust toolsets for custom AI agents and assistants

03Implementing standardized communication layers using the Model Context Protocol (MCP)

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add claudiodearaujo/izacenter agent-tool-builder

For use in Claude.ai and ChatGPT