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An AI agent uses a tool by requesting a specific operation—such as searching a database or sending a message—in a structured format. The surrounding application or runtime, not the model itself, executes the request and returns the result. The model can then use that result to answer the user or decide whether another tool call is needed.
How does an AI agent use a tool?
A tool is a capability that an application makes available to a model. It might retrieve current information, look up a business record, update a system, or hand work to another agent. The developer defines what each tool does and what inputs it accepts. The model can request an available tool, but its request does not by itself provide the credentials or authority to perform the operation.
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The tool-call loop
- The application provides tool definitions. These describe the available operations and may specify an input schema, such as a required city name for a weather lookup.
- The model decides whether a tool is useful. If so, it returns a structured request naming the tool and supplying arguments. It may instead respond directly without calling a tool.
- The runtime or application executes the request. It validates the inputs, applies its access controls, and invokes the relevant program or service.
- The result is returned to the model. The model can use the returned data to produce an answer or make another tool request.
- The process continues until the task is complete. A user request may require several calls before the model has enough information or has completed the requested action.
This is a handoff to software with the capability to perform the operation—not just text in which a model claims it has done something. The exact execution arrangement depends on the provider and integration. See the OpenAI function-calling guide and Anthropic’s tool-use overview.
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What are practical examples of AI tool use?
Tools can supply information, change a system, or coordinate work. OpenAI’s practical guide groups them as data, action, and orchestration tools; a workflow may use more than one kind. See the OpenAI practical guide to building agents.
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Retrieve current information
A weather tool can accept a city, retrieve conditions from a weather service, and return the data for the model to summarize. Without an appropriate data source, the model should not be treated as having retrieved current conditions.
Read a business record
A data tool can search a transaction database or customer-management system and return relevant account details. The model can use that response to answer a question, subject to the application’s permissions and the data it exposes.
Change a system or communicate
An action tool can update a customer record, send a message, or route a support ticket to a person. The application executes the operation; the model’s structured request is not proof that the change succeeded. A reliable workflow should use the returned result to determine what happened.
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An agent might retrieve a meeting transcript from a drive, extract relevant details, then call a customer-management tool to attach notes to a lead. This is a multi-step workflow: the output from one call informs the next. Passing a large transcript through the model repeatedly can consume context, so an execution environment may process the full content and return only the relevant summary or fields. Anthropic describes this approach in its article on code execution with MCP.
Delegate a part of the work
A larger agent workflow can expose a specialist research or writing agent as a tool. The coordinating agent can request that specialist’s work and use its result alongside other tool outputs.
Function calling and MCP: what is the difference?
Function calling is a pattern in which a model requests a defined function, often with arguments constrained by a schema. Application code or a runtime carries out the operation and returns its result. The term describes how the model expresses a request; it does not imply that the model itself runs the function.
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The Model Context Protocol (MCP) is a server-oriented integration pattern. An MCP server publishes tool definitions and handles calls, while a compatible agent runtime can discover those tools and pass their results back to the model. Implementations vary: a connection may be handled by a hosted service, run in the agent’s environment, or use a local process. The MCP architecture documentation explains the protocol’s structure.
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These approaches are not mutually exclusive descriptions of every part of a system: function calling describes a model’s structured request, while MCP provides a way for compatible clients and servers to expose and handle tools. The specific execution location and configuration depend on the platform. For examples, see Anthropic’s tool-use documentation and the OpenAI remote MCP guide.
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How should developers choose an integration approach?
Compare the approaches against the workflow’s needs rather than treating any one connection pattern as universally best.
- Capability: Decide whether the workflow needs to retrieve information, change a system, or delegate work. Expose only the operations it needs.
- Execution location: Establish whether the application, a provider-hosted service, or a local environment will run the tool. This affects network access, control, and where processing takes place.
- Definition and discovery: Consider whether tools are defined in a request, discovered from an MCP server, or loaded only when needed. The platform determines which options and controls are available.
- Permissions and approvals: Identify which tools may be discovered and called, what credentials execution code can use, and whether consequential actions require human approval. OpenAI’s MCP documentation describes an
allowed_toolscontrol for limiting tool discovery and calls; available mechanisms vary by integration. See the remote MCP guide. - Data movement and context: Estimate how much result data must be returned to the model at each step. Processing large intermediate content outside the model and returning only what is needed can reduce context use and avoid unnecessary copying.
- Interface quality: Make tool definitions reusable, standardized, documented, and tested. Clear descriptions and input schemas help establish a dependable contract between the model and the executing code.
How can tool use be made safer and more reliable?
Treat every model-generated call as an untrusted request to evaluate, not as authorization. The code that executes the operation must validate arguments and enforce permissions. A tool schema can constrain the shape of an input, but it does not replace checks that the requested operation is allowed for that user and context.
- Expose only the tools needed for the task, and limit which ones can be discovered or called.
- Check inputs and permissions in the execution layer before reading sensitive records or making changes.
- Use a human approval step for actions whose consequences warrant it.
- Return a clear success, failure, or result from execution so the model does not have to infer whether an action occurred.
- For sensitive records or large content, decide where processing happens and minimize unnecessary data returned to the model.
These principles apply whether a call is handled by application code, a hosted tool, or an MCP connection; the controls available to implement them are platform-specific.
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