What is Tool Use? (Function Calling)

Tool use lets AI access external tools, APIs, and databases to complete tasks. Learn how function calling works and why it matters for brand visibility.

The capability of AI systems to call external tools, APIs, and services to gather information or complete tasks beyond their training data.

Tool use transforms AI from a static knowledge system into an active assistant that can browse the web, run code, query databases, make API calls, and interact with external services. When ChatGPT uses Bing to look up current stock prices or Claude writes and executes Python code, that's tool use in action. It's the bridge between what an AI knows and what it can do.

Deep Dive

Tool use is the mechanism that allows an AI model to interact with external software, services, or data sources during a conversation. Instead of relying solely on the information encoded in its parameters during training, the model can issue a structured request to an outside system, receive a result, and incorporate that result into its response. This capability turns a language model from a closed, static knowledge base into an open, dynamic assistant that can perform actions and access fresh information. The model itself does not execute the tool; it generates a description of the desired call, and the surrounding application handles the actual execution and returns the output to the model for synthesis. For businesses, tool use changes how AI systems gather and present information about products, services, and brands. When a potential customer asks an AI assistant for a recommendation, the assistant may use a web browsing tool to check current reviews, a database tool to verify pricing, or a code execution tool to compare specifications. The AI becomes an active researcher, and the sources it consults directly shape the answer the customer receives. This makes visibility in tool-accessible sources a new priority for marketing and SEO teams. If your product information is not structured in a way that these tools can retrieve and interpret, you risk being omitted from AI-generated recommendations entirely. The technical process typically follows a standard pattern. First, the AI model is given a set of tool definitions, each describing a function it can call, the parameters it requires, and the type of result it returns. When a user's request triggers the need for external information, the model outputs a structured object, usually in JSON format, specifying which tool to invoke and with what arguments. The surrounding application executes the call, passes the result back to the model, and the model then generates a final response that integrates the retrieved data. This cycle can repeat multiple times in a single conversation, allowing the model to gather information from several tools before formulating a comprehensive answer. Consider a customer asking an AI assistant, "What's the best project management software for a small remote team?" Without tools, the model can only draw on its training data, which may be months old and lack specific product details. With tool use, the model can issue a web search query, retrieve current comparison articles and user reviews, and then synthesize a recommendation based on up-to-date information. It might also call a pricing API to confirm costs or a code tool to analyze feature matrices. The assistant could even check the availability of free trials by querying the vendors' websites, providing a level of detail and timeliness that a static model cannot match. Another example involves internal business data. A support chatbot equipped with tool use can query a company's order database when a customer asks about a shipment. The model recognizes the intent, calls a function like `get_order_status(order_id)`, receives the real-time status, and relays it in natural language. This avoids the need to train the model on constantly changing order data and ensures accuracy. Similarly, a sales assistant could use a CRM tool to look up a lead's interaction history before drafting a follow-up email, personalizing the message based on recent touchpoints without requiring the model to memorize customer records. Tool use is closely related to several other AI concepts. Retrieval-Augmented Generation (RAG) is a specific form of tool use where the external tool is a document retrieval system. The model queries a vector database, receives relevant text chunks, and grounds its answer in those sources. Real-time AI search is another common tool, enabling models to fetch current web content. AI agents extend tool use by chaining multiple tool calls together, allowing the model to plan and execute multi-step tasks like researching a topic, writing a report, and emailing it. While tool use focuses on single interactions, agents orchestrate sequences of tool calls to achieve broader goals. A common misunderstanding is that tool use gives AI unrestricted access to any system. In reality, each tool must be explicitly defined, integrated, and authorized. The model can only call functions that developers have registered and secured. Another misconception is that tool results are always accurate. Web browsing tools may retrieve cached or incomplete pages, APIs can return errors, and the model can misinterpret structured data. The reliability of a tool-using AI depends on the quality of the tools and the robustness of the integration. Developers must implement error handling and validation to ensure that the model does not act on faulty information. Some believe that tool use is equivalent to training the model on new data. This is incorrect. Tool use retrieves information at query time and holds it in the conversation context temporarily. It does not update the model's weights or permanently change its knowledge. Once the conversation ends, the retrieved information is discarded. Training, by contrast, permanently alters the model's behavior and knowledge base. This distinction is crucial for data privacy and compliance, as tool-retrieved data is ephemeral and does not become part of the model's long-term memory. For marketers, the rise of tool use means that AI assistants are now active curators of information. When an AI browses the web to answer a query, it selects which sources to trust and cite. A product comparison powered by real-time search will pull from specific review sites, competitor pages, and pricing databases. Your brand's presence on those sources becomes newly critical. Traditional SEO optimized for human search engine users. Now you must also consider how your information appears when AI tools query it programmatically. Structured data, clear API documentation, and authoritative content are essential to ensure that AI tools can find and correctly interpret your brand's information. Looking ahead, tool use is becoming a standard feature of major AI platforms. Providers offer pre-built tools for common tasks like web search and code execution, while allowing enterprises to register custom functions. This capability is foundational for AI agents that autonomously complete complex workflows. As tool use matures, the ability to monitor and influence how AI systems retrieve and present information will become a key competitive advantage. Organizations that proactively structure their data and content for AI tool consumption will be better positioned to appear in the responses that shape customer decisions.

Why It Matters

Tool use fundamentally changes how AI gathers and synthesizes information about your brand. When a customer asks an AI assistant for product recommendations, that AI might browse your website, check review platforms, query pricing databases, and pull from news sources-all in seconds. This creates a new visibility imperative: being present and accurate in tool-accessible sources. Traditional SEO optimized for human search. Now you also need to consider how your information appears when AI tools query it programmatically. The AI selecting which sources to include becomes a critical intermediary between your brand and potential customers.

Examples

In a product strategy meeting discussing AI capabilities: ChatGPT's tool use capabilities are getting serious. It can now browse the web, run code, and even interact with third-party services through plugins. We need to think about how our information appears when these tools query it.

During a technical architecture discussion: We're implementing function calling so the AI can query our inventory database in real-time. When customers ask about availability, the model will make a structured API call rather than guessing based on outdated training data.

In a competitive analysis review: I tested Perplexity's tool use on our competitor queries. Its web browsing tool pulls heavily from G2 reviews and official product pages. If we're not optimized for those sources, we're invisible when the AI does its research.

Common Misconceptions

Misconception: Tool use means AI can access any system or database. Reality: AI can only use tools explicitly configured and authorized. Each tool requires specific integration work, authentication setup, and permission controls. An AI cannot arbitrarily access systems it hasn't been connected to.

Misconception: AI tools always return accurate, current information. Reality: Tool reliability varies significantly. Web browsing might return cached results, APIs can timeout or error, and the AI can misinterpret returned data. Results require the same scrutiny as any external data source.

Misconception: Plugins and tool use are the same as AI training. Reality: Tool use retrieves information at query time without changing the model's underlying knowledge. Unlike training, which permanently shapes the model, tool results are temporary context for a single conversation.

Key Takeaways

Tools extend AI beyond static training data: Without tools, AI knowledge is frozen at training time. Tool use enables real-time information access, code execution, and integration with live systems and databases.

Function calling is the standard mechanism: AI outputs structured JSON specifying which tool to call and with what parameters. This standardized approach is now universal across major providers.

Web browsing tools select sources on your behalf: When AI searches the web, it chooses which sources to trust and cite. Your brand's presence in tool-accessible sources directly affects whether you appear in AI responses.

Agents chain tools for complex tasks: Advanced AI systems combine multiple tool calls sequentially: searching, analyzing, writing, and executing. This amplifies both the capability and the importance of being in retrievable sources.

Related Terms

AI Agent: Another entry in the AI models cluster connected to Tool Use.

Zero-Shot Learning: Another entry in the AI models cluster connected to Tool Use.

Inference: Another entry in the AI models cluster connected to Tool Use.

Knowledge Cutoff: Another entry in the AI models cluster connected to Tool Use.

RAG: Another entry in the AI models cluster connected to Tool Use.

System Prompt: Another entry in the AI models cluster connected to Tool Use.

Prompt Engineering: Another entry in the AI models cluster connected to Tool Use.

ChatGPT: Another entry in the AI models cluster connected to Tool Use.

Gemini 2.0: Another entry in the AI models cluster connected to Tool Use.

Open Source AI: Another entry in the AI models cluster connected to Tool Use.

Prompt: Another entry in the AI models cluster connected to Tool Use.

Track visibility where AI tools look

When AI systems use web browsing tools to answer queries about your industry, they're pulling from specific sources and forming responses that shape user perceptions. Trakkr monitors how your brand appears in these AI-generated responses, helping you understand which sources the AI trusts and where gaps in your visibility exist. Feature: AI Search Monitoring