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agent-tool-builder

Generates MCP patterns and JSON Schemas for AI agent tool design.

Updated Aug 23, 2026
Search Tags:#validation#MCP#error-handling#tool-design#agent-tools#JSON-Schema#LLM-tools
One-click install
npx skills add https://github.com/gonzoblasco/antigravity-developer-stack --skill agent-tool-builder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-tool-builder
Source: https://github.com/gonzoblasco/antigravity-developer-stack/tree/main/ai-agents/agent-tool-builder
Command: npx skills add https://github.com/gonzoblasco/antigravity-developer-stack --skill agent-tool-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

The Agent Tool Builder empowers AI engineers to design, validate, and standardize tools for AI agents. It focuses on JSON Schema quality, MCP-based tool patterns, and structured error handling to ensure reliable tool interactions and predictable LLM behavior.

Core Features & Use Cases

  • Generate precise input schemas and clear tool descriptions that guide LLMs
  • Provide MCP patterns, templates, and validation scripts to accelerate development
  • Create tools, resources, and prompts with consistent error handling and testing

Quick Start

Example: "Create an MCP tool named note_manager with inputs for action, note_id, title, content, and tags, and verify the output structure." Then run the included scripts to scaffold the schema, validate using the validator, and test with the sample manage_notes workflow.

Frequently Asked Questions about agent-tool-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design MCP tools with clear JSON Schemas for LLM agents?▼

To design MCP tools for LLM agents, you generate explicit input and output contracts using JSON Schema. This enforces input validation, structured error formats, and predictable tool interactions across the full agent tool lifecycle.

What is the best way to standardize error handling for AI agent tools?▼

Standardizing error handling for AI agent tools involves creating consistent error formats and validation scripts. This ensures reliable tool interactions and predictable LLM behavior during runtime input validation.

How do I create an MCP tool with input validation and output structure verification?▼

You create an MCP tool by defining inputs like action and content, then scaffold the JSON Schema. Run included validation scripts to verify the output structure and test the workflow with provided templates.

Do I need MCP patterns and templates to build reliable tools for AI agents?▼

Yes, using MCP patterns and templates accelerates development of reliable AI agent tools. They provide standardized schemas, prompts, and resources that guide LLMs and enforce explicit tool contracts.

Can I use agent-tool-builder to scaffold schemas for resources and prompts?▼

Yes, you can scaffold schemas for resources, prompts, and tooling. The Skill provides comprehensive templates that cover the full MCP tool lifecycle, ensuring consistent design and runtime validation.

Why does my LLM tool return unpredictable outputs without JSON Schema validation?▼

LLM tools return unpredictable outputs without JSON Schema validation because explicit input and output contracts are missing. Enforcing schemas and standardized error formats ensures reliable interactions and predictable behavior.

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