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Generador de Esquemas de Funciones GPT

Generar esquemas de function calling de OpenAI para modelos GPT con JSON Schema

No parameters added yet
{
  "type": "function",
  "function": {
    "name": "",
    "description": "",
    "parameters": {
      "type": "object",
      "properties": {},
      "required": []
    }
  }
}

How Generador de Esquemas de Funciones GPT Works

A Function Schema Builder is a developer utility used to define, validate, and export structured tool definitions for LLM instrumentation. This tool is essential for AI engineers, backend developers, and plugin creators converting Python/TS function signatures into the strict JSON Schema format required by OpenAI's 'tools' parameter, defining enums and required fields, and debugging 'Invalid Request' errors in their agent loops.

The generation engine handles schema construction through a rigorous definition pipeline:

  1. Signature Parsing: The tool takes your inputs (Function Name, Description, Parameters).
  2. Type Mapping: The engine maps your logical types to JSON Schema Types:
    • String -> {"type": "string"}
    • Integer -> {"type": "integer"}
    • Enum -> {"type": "string", "enum": [...]}
    • Object/Array -> Nested definitions.
  3. Attribute Injection: The tool appends necessary Validation Constraints:
    • description: Vital for the LLM to understand when to use the tool.
    • required: The list of mandatory parameters.
  4. Reactive Real-time Rendering: Your "Ready-to-Paste JSON" updates instantly as you add parameters or toggle 'Strict Mode'.

The History of Tool Use: From Chat to Action

How we enable AI to "Do" things, not just "Say" things.

  • The Chatbot (2022): AIs were isolated boxes. They could talk, but couldn't check the weather or book a flight.
  • Plugins (March 2023): OpenAI introduced Plugins, allowing AIs to call external APIs.
  • Native Function Calling (June 2023): Models were fine-tuned to output JSON instead of text when they needed to use a tool. This required a Very specific JSON Schema format. This tool Automates the creation of that schema.

Technical Comparison: Schema Properties

Understanding "Description Engineering" is vital for Agent Reliability.

Property Purpose Example Workflow Impact
Name Identifier get_stock_price Clarity
Description Instruction "Get the current price for a ticker" Accuracy
Enum Constraint ["USD", "EUR"] Safety
Required Validation ["ticker"] Reliability
Strict Mode true Determinism

By using this tool, you ensure your AI Agents can reliably interact with the real world.

Security and Privacy Considerations

Your schema design is performed in a secure, local environment:

  • Local Logical Execution: All JSON generation and type mapping are performed locally in your browser. Your internal function signatures—which reveal your backend's capabilities—never touch our servers.
  • Zero Log Policy: We do not store or track your inputs. Your API Designs and Tool Configurations remain entirely confidential.
  • W3C Security Compliance: The tool operates within the standard browser sandbox, ensuring no interaction with your local file system or Private Metadata.
  • Privacy First: To maintain absolute Data Privacy, the tool functions as an anonymous utility.

Frequently Asked Questions

Function Calling is a feature where an LLM (like GPT-4) says "I need to run the get_weather function" instead of just talking. It outputs a JSON object that your code can execute.

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