Definition
Function Calling (also known as Tool Use) is the AI capability that bridges the gap between language understanding and real-world action. It enables AI models to recognize when they need external capabilities—checking a database, calling an API, running code, searching the web—and invoke those capabilities to accomplish tasks that pure text generation cannot.
To understand function calling's importance, consider what AI models can and cannot do natively. A language model can discuss weather patterns, explain meteorology, and even predict general seasonal trends based on training data. But it cannot tell you whether it will rain tomorrow in your city—that requires accessing a weather API. Function calling enables the AI to recognize 'I need current weather data,' invoke a weather API, process the response, and provide an accurate, timely answer.
The mechanism works roughly like this:
- Function Definition: Developers define functions the AI can call, including their purposes and parameters
- Recognition: During conversation, the AI recognizes when a function would help answer the user's request
- Call Generation: The AI generates a structured function call with appropriate parameters
- Execution: The system executes the function and returns results to the AI
- Response Integration: The AI incorporates function results into its response
This transforms AI from a passive text generator into an active system that can retrieve real-time information, perform calculations, access databases, interact with services, and take meaningful actions.
In 2025, function calling has become a foundational AI capability:
OpenAI: Function calling is core to GPT-4 and the Assistants API, enabling custom tools and integrations Anthropic: Claude's tool use capabilities enable external integrations and computer use Google: Gemini's function calling powers integrations with Google services and custom APIs Open Source: Frameworks like LangChain provide function calling for various models
Business applications of function calling are extensive:
Customer Service: AI that can check order status, process returns, schedule appointments Business Intelligence: AI that can query databases, run analyses, generate reports Software Development: AI that can execute code, run tests, interact with development tools Sales Operations: AI that can update CRM records, schedule meetings, send follow-ups Research: AI that can search databases, retrieve papers, compile information
For GEO and content strategy, function calling has significant implications. AI systems with function calling capabilities can access external content sources in real-time. Content that's accessible through APIs, structured for programmatic access, or exposed through tools becomes directly accessible to AI systems during their workflows.
Consider how this changes content discovery. Without function calling, AI relies on training data and optional web browsing. With function calling, AI can directly query content databases, access specialized information services, and retrieve specific content on demand. Businesses that make their content accessible through functions and APIs create new pathways for AI discovery and citation.
Key considerations for function calling optimization:
API Accessibility: Content exposed through APIs becomes accessible to AI with function calling Structured Data: Well-structured content is easier for AI to retrieve and process through functions Real-Time Information: Function calling enables access to current information beyond training data Integration Opportunities: Consider creating functions that expose your content to AI systems
Function calling also enables the agentic workflows transforming how AI accomplishes complex tasks. Agents use function calling to interact with the world—browsing, coding, data accessing, and action-taking all depend on the ability to invoke external functions.
The evolution of function calling points toward more sophisticated tool use, standardized protocols (like MCP) for AI-tool interaction, and increasing integration between AI systems and business applications. Understanding and leveraging function calling will be important for businesses wanting their content and services accessible to AI systems.
Examples of Function Calling / Tool Use
- A customer service AI uses function calling to check inventory systems when users ask about product availability, access CRM data for order status inquiries, and process return requests through backend systems—providing complete service rather than just informational responses
- A financial analysis AI calls market data APIs for current prices, executes calculation functions for portfolio analysis, queries historical databases for trend data, and generates visualizations through charting functions—delivering actionable financial insights grounded in real data
- A coding assistant uses function calling to execute code snippets for testing, call documentation APIs for accurate reference information, interact with version control systems, and run linting tools—providing development assistance that goes beyond code generation
- A research AI calls academic database APIs to retrieve relevant papers, executes search functions across multiple sources, uses summarization functions for lengthy documents, and accesses citation databases—conducting genuine research rather than relying solely on training data
- A content platform creates functions that expose their content to AI systems. When AI assistants need information in the platform's domain, they can call these functions to retrieve and cite current, authoritative content—creating a new discovery channel beyond traditional search
