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AI agents use tools by receiving a description of available capabilities, choosing one when it helps answer a request, and sending a structured call for an application or provider service to execute. Function calling is one way to format that request; it does not mean the model itself has automatically run the function or gained unrestricted access to your systems.
What are AI agent tools?
A tool is a capability made available to a model through a request-and-response interface. Examples include retrieving a record from a database, checking the weather, updating a customer record, or asking another agent to perform a task. The model can select a tool and supply arguments, but the system that hosts the integration determines whether and how the requested operation runs.
Tools commonly fit three roles: data tools retrieve information, action tools change something in a system, and orchestration tools delegate work to another agent. The distinction matters: searching a database is generally read-only, while changing an account or sending a message can have side effects. OpenAI describes these categories in its practical guide to building agents.
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How does function calling work?
Function calling—also called tool calling—lets a model request an operation in a structured format. A tool definition describes the function and its expected inputs. The model can then return a tool name and arguments, after which the application or a provider-hosted service handles execution. The result is passed back to the model, which can respond to the user or request another tool.
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- Expose a tool definition. For example, describe
get_weather(location)and specify what a valid location argument looks like. - Send the user’s request and available tool definitions to the model. The model decides whether a tool would help.
- Receive a structured call. If the model chooses the weather tool, it might return the tool name and a location argument.
- Validate and execute the request. Application code checks the arguments and permissions, then calls the weather service. The schema guides the model; it does not perform the operation.
- Return the result to the model. The application associates the result with the corresponding tool call, and the model uses it to continue the conversation.
The exchange can repeat if the model needs another tool before answering. OpenAI documents this lifecycle in its function calling guide. Anthropic likewise illustrates a tool-use request, application execution, and returned tool result in its Claude tool use documentation.
Does the AI actually execute the function?
Not necessarily. In a typical client-side integration, the model emits a structured request and the application executes it. The application remains responsible for checking inputs, applying authorization rules, calling external systems, and returning results. A valid-looking call is not proof that an operation was authorized or performed.
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Some products also provide tools that run on the provider’s infrastructure. Anthropic distinguishes tools executed by the client application from server tools executed on Anthropic infrastructure. OpenAI’s MCP documentation describes connection options including service-origin, environment-origin, and stdio connections. These arrangements affect where execution occurs and which system handles credentials; they should not be treated as interchangeable. See OpenAI MCP connections for its documented connection choices.
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Function calling describes a structured way for a model to request a function or tool. MCP—the Model Context Protocol—provides a pattern for connecting an AI application to tool servers that expose capabilities and context. In short, function calling concerns the model’s structured request; MCP concerns a connection protocol and server pattern that can make tools available. An application may use MCP to discover or connect to tools and still have provider-specific rules for how the model requests them and how the application executes them.
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Support is not uniform across providers. Google’s Gemini documentation says its remote MCP support requires Streamable HTTP and does not support SSE. That constraint applies to the documented Gemini integration, not to every MCP implementation. Check the current provider documentation for supported transports, models, and configuration before building against them; Google’s Gemini function-calling guide describes its current interface.
What changes between providers?
The shared idea is a model request followed by tool handling, but tool definitions and execution details differ. OpenAI documents JSON-schema function tools as well as custom free-form tools; Anthropic’s user-defined tools use an input_schema. The exact schema, response format, available tool types, and execution arrangement depend on the provider and product.
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| Implementation detail | Documented example | What to verify |
|---|---|---|
| Tool definition | OpenAI documents JSON-schema function tools and custom free-form tools; Anthropic documents user-defined tools with input_schema. |
Required fields, validation behavior, tool-call response format, and supported models. |
| Execution location | Anthropic distinguishes client-executed tools from server tools; OpenAI documents multiple MCP connection origins and stdio. | Which system runs the operation, handles failures, and has access to credentials. |
| Protocol and transport | Gemini remote MCP support requires Streamable HTTP and does not support SSE. | Whether the provider and MCP server support the same transport and authentication approach. |
| Access control and credentials | OpenAI documents allowed_tools, HTTP credentials, and vault credentials for supported connections. |
How tool access is restricted, where secrets are stored, and what appears in logs. |
| Operational controls | Controls vary by provider and product; there is no single behavior established across these examples. | Approvals, logging, timeouts, error handling, and a way to stop consequential actions. |
How do you give an AI agent tool access safely?
A tool schema helps a model format a request, but it does not replace authorization, validation, or safeguards for actions with side effects. Treat every tool call as an input to a system you control.
- Expose only necessary capabilities. Keep the available tool set narrow, and restrict which tools the model can discover or call. OpenAI documents an
allowed_toolscontrol for MCP connections. - Validate arguments in application code. Check types, ranges, identifiers, and other business rules before making a call. Do not assume a schema alone makes input safe.
- Enforce permissions outside the model. Use the same authorization checks you would apply to any application request; a model-generated call should not grant itself access.
- Protect credentials. Keep secrets out of model-generated code and reusable tool definitions where possible. OpenAI cautions against placing secrets in reusable definitions and logs; its documented credential options depend on the supported connection.
- Review consequential actions. Require appropriate human approval before irreversible or high-impact changes, and provide a way to stop the process.
- Plan for operational failure. Define timeouts, error handling, and logging appropriate to the task, and test the tool definitions thoroughly. Clear, standardized definitions improve the model’s ability to select and call a tool correctly, but do not guarantee correct outcomes.
Oversight features vary across deployed products. The MIT AI Agent Index research team’s 2026 report on its selected sample of 30 agents found that 20 documented pause or stop mechanisms; this is a count within that sample, not an estimate for the entire market. The same report found 20 of 30 supported MCP. Neither figure establishes how all agent products behave. See The 2025 AI Agent Index.
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