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Function Call

Enables LLMs to call external tools and APIs via JSON Schema-defined functions.

2|Updated Jan 12, 2026
Search Tags:#llm#agent development#api integration#tool use#json schema#function calling
One-click install
npx skills add https://github.com/Lysssyo/Lysssyo.github.io --skill function-call
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Function Call
Source: https://github.com/Lysssyo/Lysssyo.github.io/tree/main/docs/80-%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD
Command: npx skills add https://github.com/Lysssyo/Lysssyo.github.io --skill function-call

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables Large Language Models (LLMs) to interact with external tools and APIs, allowing them to fetch real-time data, perform actions, and return structured information, bridging the gap between AI and the real world.

Core Features & Use Cases

  • Tool Definition: Describe functions and their parameters using JSON Schema for LLMs to understand.
  • Intent Recognition: LLMs decide when and which tool to call based on user queries.
  • Local Execution: Your code parses LLM's output and executes the actual functions.
  • Contextual Feedback: The results of tool execution are fed back to the LLM for a coherent response.
  • Use Case: An LLM can check the weather in a specific city by calling a weather API, or calculate a tax amount by invoking a financial function, all orchestrated through this Skill.

Quick Start

Use the Function Call skill to get the current weather for "New York".

Frequently Asked Questions about Function Call

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

FAQPage Schema
How do I enable LLMs to interact with external APIs using function calling?▼

You enable LLMs to interact with external APIs by defining tools via JSON Schema, allowing the model to generate structured function calls that your local code parses and executes for deterministic task execution.

What is the best way to define tools for LLM agent development?▼

The best way to define tools for LLM agent development is using JSON Schema to describe functions and parameters, enabling intent recognition so the model decides precisely when and which API to invoke.

Can I execute multiple API calls simultaneously with LLM function calling?▼

Yes, you can execute multiple API calls simultaneously because this function calling approach supports parallel tool invocation, allowing advanced agentic behavior to retrieve dynamic data efficiently.

How does an LLM handle contextual feedback after a tool execution?▼

After local execution of a tool call, the results are fed back to the LLM as contextual feedback, enabling the model to generate a coherent final response based on the external data.

Do I need OpenAI to use this function calling Skill for structured output generation?▼

Yes, you need OpenAI as a dependency to use this Skill, which facilitates structured output generation and precise tool_choice control for your agentic behavior workflows.

How to control which function the LLM calls during agent development?▼

You control which function the LLM calls by using precise tool_choice parameters during function calling, ensuring deterministic task execution and accurate intent recognition for your specific use case.

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