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Agent Tool Builder Skill · Data Ai

Agent Tool Builder – Design Tools That AI Agents Actually Use

Master tool design for AI agents with 6 core capabilities.

Reduce token costs by 10x and eliminate silent failures. Download the skill now.

  • JSON Schema
  • MCP
  • Function Calling
  • Error Handling
  • Agent Tools
  • Validation

About This Skill

This skill covers 6 core capabilities including tool schema design, MCP standards, and error handling. Learn why tool descriptions matter more than implementations and how to keep your agent under 20 tools for optimal performance.

Quick Start

  1. 1Define your tool schema using JSON Schema best practices
  2. 2Write descriptions that guide LLM behavior, not just document parameters
  3. 3Implement validation gates that reject, fix, or escalate before execution
Example Command
npx antigravity install agent-tool-builder

Core Capabilities

Agent Tools

Design tools that AI agents can reliably call, with clear schemas and descriptions that reduce token waste.

Function Calling

Implement function calling patterns that work with Anthropic SDK and other major frameworks.

Tool Schema Design

Master JSON Schema for tool definitions, ensuring LLMs understand parameters and return types.

MCP Tools

Build reusable, cross-platform tools using the Model Context Protocol standard.

Tool Validation

Implement validation gates that catch errors before execution, preventing silent failures.

Tool Error Handling

Design explicit error handling that returns strings for LLM consumption, not silent failures.

Usage Examples

Before

{"name":"get_weather","parameters":{"city":"string"}}

After

{"name":"get_weather","description":"Fetch current weather for a city. Use when user asks about weather, temperature, or conditions. Returns a string with temperature and conditions.","parameters":{"type":"object","properties":{"city":{"type":"string","description":"City name, e.g. 'London' or 'New York'"}},"required":["city"]},"returns":"string"}

Input

How do I handle an API failure in my tool?

Output

Return a descriptive error string like 'Error: Unable to fetch weather data for city XYZ. The API returned a 503 status.' instead of throwing an exception.

Input

What is the MCP standard and how do I use it?

Output

MCP (Model Context Protocol) is Anthropic's open standard for defining tools that work across platforms. Use it to make your tools reusable with any MCP-compatible agent.

SKILL.md

---
name: agent-tool-builder
description: Tools are how AI agents interact with the world. A well-designed
  tool is the difference between an agent that works and one that hallucinates,
  fails silently, or costs 10x more tokens than necessary. This skill covers
  tool design from schema to error handling.
risk: unknown
source: vibeship-spawner-skills (Apache 2.0)
date_added: 2026-02-27
---

# Agent Tool Builder

Tools are how AI agents interact with the world. A well-designed tool is the
difference between an agent that works and one that hallucinates, fails
silently, or costs 10x more tokens than necessary.

This skill covers tool design from schema to error handling. JSON Schema
best practices, description writing that actually helps the LLM, validation,
and the emerging MCP standard that's becoming the lingua franca for AI tools.

Key insight: Tool descriptions are more important than tool implementations.
The LLM never sees your code - it only sees the schema and description.

## Principles

- Description quality > implementation quality for LLM accuracy
- Aim for fewer than 20 tools - more causes confusion
- Every tool needs explicit error handling - silent failures poison agents
- Return strings, not objects - LLMs process text
- Validation gates before execution - reject, fix, or escalate, never silent fail
- Test tools with the LLM, not just unit tests

## Capabilities

- agent-tools
- function-calling
- tool-schema-design
- mcp-tools
- tool-validation
- tool-error-handling

## Scope

- multi-agent-coordination → multi-agent-orchestration
- agent-memory → agent-memory-systems
- api-design → api-designer
- llm-prompting → prompt-engineering

## Tooling

### Standards

- JSON Schema - When: All tool definitions Note: The universal format for tool schemas
- MCP (Model Context Protocol) - When: Building reusable, cross-platform tools Note: Anthropic's open standard, widely adopted

### Frameworks

- Anthropic SDK - When: Claude-based agents Note: Beta tool run

Frequently Asked Questions

FAQ

Which AI agent frameworks are compatible with this skill?
This skill covers tool design that works with any LLM-based agent, including Claude (via Anthropic SDK), OpenAI, and custom agents. The MCP standard ensures cross-platform compatibility.
Who is this skill for?
This skill is for developers building AI agents, whether for personal projects, SaaS products, or enterprise systems. It's ideal for anyone who wants to reduce token costs and improve agent reliability.
How is this different from standard API design?
Unlike traditional API design, agent tool design prioritizes description quality over implementation. The LLM never sees your code—only the schema and description. This skill focuses on writing descriptions that actually guide LLM behavior.
What programming languages are supported?
The skill is language-agnostic. JSON Schema and MCP are universal standards. Examples use Python and JavaScript, but the principles apply to any language used for agent development.
What results can I expect after learning this skill?
You'll be able to design tools that reduce token usage by up to 10x, eliminate silent failures, and improve agent accuracy. You'll also be able to build reusable tools using the MCP standard.

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Trigger Phrases

Use these phrases to activate this skill in your AI coding assistant:

tool design for agentsfunction calling best practicesMCP standardJSON Schema for LLMagent tool validationreduce token costs