A language model that only generates text is limited in what it can do. As soon as you give it the ability to call tools, the character of the system changes completely. Tool use is the technique that gives AI agents access to the world beyond the model itself.
Most large language models now support tool use, also called function calling or tool calling. The concept is straightforward: instead of generating a text response, the model can indicate that it wants to call an external function. The application layer then executes that function and returns the result to the model. This way an AI agent can consult databases, perform calculations, read files and control external services.
With tool use you as a developer describe a set of functions the model can call, including their parameters and what they do. You do this in the system prompt or via the API of the model provider (such as OpenAI, Anthropic or Google). The model reads these descriptions and learns when to use them.
When the model decides it needs a tool, it does not generate text but a structured call: the name of the tool and the parameters it wants to pass. The application code receives that call, executes the actual function, and sends the result back to the model. The model processes that result and continues.
This is a fundamentally different architecture from a model that only generates text. The model is now a decision-maker that delegates execution to external code.
The types of tools are diverse. Some common categories:
The choice of tools largely determines what an agent can do. An agent without write access cannot save anything. An agent without a search tool works only on the information in its context.
A well-defined tool has three elements: a clear name, an accurate description and well-typed parameters. The model uses the name and description to decide whether the tool is relevant for the current task. The parameters determine what information the model must supply.
Poorly written tool descriptions lead to misuse. If a description is vague, the model may call the tool at unintended moments or fail to supply the correct parameters. Precision in the definition pays off.
Tool use introduces risks that do not arise with a purely text-generating model. An agent with write access to a database can make mistakes that damage data. An agent with access to email can send messages that were not intended.
An important risk is prompt injection: malicious text in a web page or document that the agent reads attempts to give the agent instructions outside its intended task. Good sandboxing, minimal permissions (least privilege) and human-in-the-loop for irreversible actions are the standard defenses.
It is wise to restrict tools to what is strictly necessary for the task. An agent that only reads does not need write access. An agent that searches internal documents does not need internet access.
In a complex workflow an agent calls multiple tools in sequence or in parallel. A research agent might first execute a search, then retrieve relevant pages, summarize them and store the summary. This is a chain of tool calls where the result of one step is the input for the next.
Frameworks such as LangChain, LlamaIndex and the native tool use APIs of model providers support this pattern. At Mach8 we design tool sets tailored to the specific workflow and the systems an organization already uses.
Tool use is the link that connects a language model to the systems and data an organization already has. It greatly expands the capabilities of an AI agent but also requires careful design, precise descriptions and deliberate choices about access and security.
Want to know which tools fit your processes? See our AI agents services or get in touch with Mach8.
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