Elysia Tools

Browse tools, examples, visual explainers, and topic guides from one clear workspace.

Top Categories

Hubs

Language
ZHESPTRUFRDEEN

Elysia Tools

Navigation

AI Tools

LLM Tool-Calling JSON Schema Builder

· Last updated：2026-08-28

Define an LLM tool once and emit validated function-calling payloads for OpenAI, Anthropic and Gemini.

Details

What this tool helps you do

One-line-per-parameter DSL in, three ready-to-paste provider payloads out, with a validation table that catches the schema differences that cause 400 errors before you ship.

Execution

Run this tool

Fill in the form, run the tool, and review the result in one place.

Samples

Examples that match this tool

Related

Continue with connected tools and hubs

Written by the Elysia Tools team

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

  1. 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.
  2. 2Configure provider options by toggling strict mode and selecting the desired tool_choice setting (omit, auto, or force).
  3. 3Inspect the generated parameter validation table to verify type formatting, enum constraints, and provider-specific schema requirements.
  4. 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.

Home
Tools3197
Samples593
Visualizations461
Viewers103
Hubs229
Tags333
Images, Audio & Video655

Image, audio, and video processing, conversion, and optimization tools

Math, Date & Finance644

Calculators, numerics, date logic, statistics, and finance tools

Design & Color296

Color, layout, graphics, visual styling, and design helper tools

Text & Writing209

Writing, cleanup, formatting, extraction, and text analysis tools

Developer & Web193

Developer utilities, networking, web debugging, and automation helpers

Conversion & Encoding175

Format, file, archive, unit, and encoding conversion tools

UI Screenshot Structure Review and Development Handoff
Piano Sketch Creation, MIDI Review and Listening Demo Handoff
Retro Game Texture Review and Asset Handoff
Screenshot-Based Procedure Guide Review and Handoff
Responsive Page Change Review and Evidence Handoff

© 2026 Elysia Tools. All rights reserved.

DocsPricingAboutTerms of ServicePrivacy PolicyCookiesContact
Development

Terraform Plan JSON Samples

Sample Terraform plan JSON files exported from terraform show -json style payloads for dependency visualization and change review

Data Formats

Chat Transcript JSON Samples

JSON examples for multi-role chat transcripts

Data Formats

JSON Samples

JSON (JavaScript Object Notation) format examples from simple to complex structures

Data Formats

Rich Media JSON Samples

JSON examples for popular rich text editors (TipTap, Quill, Slate)

AI Tools

HN mention sentiment: praise, critique and reading intent

Audit sentiment toward each named book or entity in pasted JSON or CSV. Separate praise, critique, reading intent and neutral mentions before counting recommendations.

AI Tools

MCP Server Contract Tester — stdio + WebSocket Transports, JSON Schema 2020-12 Tool Validation

End-to-end Model Context Protocol contract testing: JSON-RPC 2.0 initialize handshake and version negotiation over stdio (newline-delimited) and WebSocket (one message per text frame), tools/list inputSchema object-root and draft 2020-12 compilation, resources/list + read, prompts/list + get, ping, -32601 error semantics, sampling/createMessage round-trip, and framing discipline — with an embedded reference server for offline self-verification.

Design

ECharts Theme Token Extractor

Extract design tokens — colors, numbers, font sizes and strings — from an ECharts theme JSON and export them straight into your design system. Paste a theme object (the kind registered via echarts.init(dom, themeName)) and the tool walks every leaf, tagging each color (with optional named/rgb → hex normalization), spacing number, font size and string, then emits clean CSS variables, a Tailwind theme.extend config, Style Dictionary tokens.json, or SCSS variables. Bridges the gap between an ECharts visualization theme and Figma/CSS/Tailwind design tokens without copying each value by hand.

Design

Image Palette to Design Tokens

Extract a dominant color palette from an image (k-means clustering), then export it as CSS variables, SCSS variables, a Tailwind config, or JSON design tokens — with named colors and an auto-generated shade scale for each

Problem

Manually authoring deeply nested JSON schemas with array items and property ordering is slow and prone to formatting mistakes.

How to use

Set the tool name to search_flights, write the description, define origin object and passengers array using indentation, and enable Strict Mode.

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

Outcome

Generates valid multi-level schemas with complete required lists for OpenAI strict mode and uppercase types for Gemini declarations.