LLM Tool-Calling JSON Schema Builder

LLM Tool-Calling JSON Schema Builder | Online Free Tool | Elysia Tools

Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: AI Tools
Input types: text, textarea, checkbox, select
Output type: html
Sample coverage: 4
API ready: Yes

Overview

The LLM Tool-Calling JSON Schema Builder converts a single line-by-line parameter definition into validated, ready-to-use function calling payloads for OpenAI, Anthropic, and Google Gemini. It automatically generates strict schemas, handles provider-specific requirements such as Gemini property ordering and uppercase types, and provides a validation table to catch schema mismatches before sending API requests.

When to use

When building multi-provider AI agents and needing standardized tool calling schemas for OpenAI, Anthropic, and Gemini without writing JSON by hand.
When implementing strict structured outputs that require comprehensive required arrays and nullable-union handling for optional parameters.
When defining nested object and array parameter structures using simple indentation instead of deep, error-prone JSON schema boilerplate.

How it works

1Enter the tool name, description, and parameter list using the line-by-line syntax (name | type | required/optional | description), indenting child properties for nested objects or arrays.
2Configure provider options by toggling strict mode and selecting the desired tool_choice setting (omit, auto, or force).
3Inspect the generated parameter validation table to verify type formatting, enum constraints, and provider-specific schema requirements.
4Copy the generated JSON schemas directly into your OpenAI tools array, Anthropic tools configuration, or Gemini function declarations.

Use cases

Standardizing function calling definitions across hybrid agent stacks running OpenAI GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro.
Prototyping complex nested API tools with enum constraints and structured array payloads without hand-crafting verbose JSON schemas.
Generating Model Context Protocol (MCP) compatible tool declarations with verified parameter types and strict validation.

Examples

1. Weather Tool Payloads for Multiple Providers

AI Engineer

Background

An engineer is integrating a weather lookup capability across an application supporting both Claude and GPT-4o.

Problem

Writing separate JSON schemas for OpenAI and Anthropic by hand creates subtle syntax errors and 400 Bad Request responses.

How to use

Set Tool name to get_weather, enter the description, add parameters for city, unit enum, and forecast days, and select Auto for tool_choice.

Tool Name: get_weather
Tool Description: Get the current weather for a city. Use when the user asks about temperature, conditions, or whether it will rain today.
Parameters:
city | string | required | City name, e.g. "Paris"
units | enum<celsius,fahrenheit> | optional | Temperature unit for the report
forecast_days | integer | optional | Days of forecast to include (1-7)
Strict Mode: true
Tool Choice: auto

Outcome

Produces validated OpenAI, Anthropic, and Gemini tool payloads alongside a validation table highlighting nullable union handling for optional fields.

2. Nested Flight Search Schema with Arrays and Objects

Backend Developer

Background

A travel platform is creating an LLM booking agent that accepts structured airport details and multiple passenger preferences.

FAQ

How does the builder handle differences between OpenAI, Anthropic, and Gemini schemas?

It translates your single parameter definition into provider-specific formats, generating standard JSON Schema for OpenAI and Anthropic and uppercase types with propertyOrdering for Gemini.

How do I specify nested objects and arrays?

Declare the parent as object or array<object>, then indent child parameters with spaces directly beneath it.

What parameter types are supported in the DSL?

Supported types include string, number, integer, boolean, object, array<type>, and enum<val1,val2>.

What does enabling Strict Mode do?

Strict mode enforces OpenAI Structured Outputs rules by ensuring all properties are listed under required, with optional parameters represented as nullable type unions.

How does the tool_choice setting affect the output?

It sets the provider-specific tool selection behavior payload to omit the property, allow automatic model choice, or force the model to invoke the tool.

Tool Name: search_flights
Tool Description: Search for flights between two airports on a date, with optional airline filters and passenger details.
Parameters:
origin | object | required | Departure airport
  code | string | required | IATA airport code, e.g. "AMS"
  city | string | optional | City name for disambiguation
destination_airport | string | required | IATA code of the arrival airport
date | string | required | Departure date in YYYY-MM-DD format
airlines | array<string> | optional | Restrict to these airline codes
passengers | array<object> | optional | One entry per traveler
  type | enum<adult,child,infant> | required | Passenger type
  seat_preference | enum<window,aisle,middle> | optional | Preferred seat
Strict Mode: true
Tool Choice: omit

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Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Examples

1. Weather Tool Payloads for Multiple Providers

2. Nested Flight Search Schema with Arrays and Objects

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