What is Function Calling?
Function Calling is a capability of large language models that allows them to generate structured outputs specifying which functions to call and with what arguments, enabling AI systems to interact with external tools, APIs, and services in a reliable and type-safe manner.
Quick Facts
| Full Name | LLM Function Calling |
|---|---|
| Created | 2023 by OpenAI |
| Specification | Official Specification |
How It Works
Function Calling bridges the gap between natural language understanding and programmatic actions. Introduced by OpenAI in 2023 and now supported by most major LLM providers, this feature allows developers to define available functions with their parameters, and the model intelligently decides when and how to use them based on user requests. This is fundamental to building AI agents and the Model Context Protocol (MCP).
Key Characteristics
- Structured JSON output for reliable parsing
- Type-safe parameter specification with schemas
- Model decides when to call functions based on context
- Supports multiple function calls in single response
- Enables tool use and external API integration
- Foundation for AI agents and autonomous systems
Common Use Cases
- Building AI agents that interact with external services
- Creating chatbots with database access
- Automating workflows through natural language
- Implementing MCP servers and clients
- Developing voice assistants with action capabilities
Example
Loading code...Frequently Asked Questions
How does function calling differ from regular text generation?
Regular text generation produces free-form natural language, while function calling generates structured JSON output that specifies which function to call and with what arguments. This structured format enables reliable parsing and execution of actions, making it suitable for building applications that need to interact with APIs, databases, or external services programmatically.
Can the model call multiple functions in a single response?
Yes, most modern LLMs with function calling support parallel function calls, where the model can request multiple functions to be executed simultaneously. This is useful for scenarios like fetching data from multiple sources at once. The model decides based on context whether sequential or parallel calls are more appropriate.
How do I handle errors when the model calls functions incorrectly?
Implement robust error handling by validating function arguments before execution, returning clear error messages back to the model, and allowing it to retry or adjust its approach. Use JSON Schema validation to catch malformed arguments early. Include helpful error descriptions in your function responses so the model can learn from mistakes and self-correct.
What is the relationship between function calling and AI agents?
Function calling is a fundamental building block for AI agents. Agents use function calling to interact with their environment—reading data, taking actions, and responding to results. The agent loop typically involves: receive user input, decide which function(s) to call, execute them, observe results, and repeat until the task is complete. Function calling enables this tool-use capability.
How should I design function schemas for optimal model performance?
Write clear, descriptive function names and descriptions that explain when and why to use each function. Define precise parameter schemas with helpful descriptions and examples. Keep the number of available functions reasonable (under 20 for best results). Group related functionality logically and avoid overlapping functions that might confuse the model about which to choose.