> For the complete documentation index, see [llms.txt](https://docs.umbraco.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.umbraco.com/ai-in-umbraco/19.latest/using-the-api/tools/using-tools.md).

# Using Tools

Give AI models the ability to call functions using the Microsoft.Extensions.AI (M.E.AI) tool system.

Tools allow AI models to call functions during a conversation. The model decides when to use a tool based on the user's request. Tool results are fed back into the conversation for the model to formulate its final response.

## Defining a Tool

Use the `[Description]` attribute from `System.ComponentModel` to help the model understand when and how to use the tool:

{% code title="WeatherTool.cs" %}

```csharp
using System.ComponentModel;
using Microsoft.Extensions.AI;

public static class WeatherTools
{
    [Description("Gets the current weather for a given city")]
    public static string GetWeather(
        [Description("The city name")] string city)
    {
        // In production, call a real weather API
        return $"The weather in {city} is 22°C and sunny.";
    }
}
```

{% endcode %}

## Calling Chat with Tools

Create an `AIFunction` from your method and pass it via `ChatOptions`:

{% code title="ChatWithTools.cs" %}

```csharp
using Microsoft.Extensions.AI;

var tool = AIFunctionFactory.Create(WeatherTools.GetWeather);

var options = new ChatOptions
{
    Tools = [tool]
};

var messages = new List<ChatMessage>
{
    new(ChatRole.User, "What's the weather like in London?")
};

var response = await _chatService.GetChatResponseAsync(
    chat => chat.WithAlias("tool-chat").WithChatOptions(options),
    messages);
// The model calls GetWeather("London"), gets the result,
// and responds with a natural language answer.
```

{% endcode %}

## Automatic Tool Invocation

Umbraco.AI includes the `FunctionInvokingChatClient` middleware by default. This means tool calls are handled automatically:

1. The model receives the user message and available tools
2. The model decides to call a tool and returns a tool call request
3. The middleware invokes the function locally
4. The result is sent back to the model
5. The model generates its final response

You do not need to handle the tool call loop manually.

## Multiple Tools

Pass multiple tools to give the model more capabilities:

{% code title="MultipleTools.cs" %}

```csharp
var weatherTool = AIFunctionFactory.Create(WeatherTools.GetWeather);
var timeTool = AIFunctionFactory.Create(TimeTools.GetCurrentTime);

var options = new ChatOptions
{
    Tools = [weatherTool, timeTool]
};

var messages = new List<ChatMessage>
{
    new(ChatRole.User, "What time is it in Tokyo and what's the weather?")
};

var response = await _chatService.GetChatResponseAsync(
    chat => chat.WithAlias("tool-chat").WithChatOptions(options),
    messages);
```

{% endcode %}

## Tools with Complex Parameters

Tools can accept complex parameter types:

{% code title="ComplexTool.cs" %}

```csharp
public static class ContentTools
{
    [Description("Searches for content by type and keyword")]
    public static async Task<string> SearchContent(
        [Description("The Document Type alias to search")] string contentType,
        [Description("Search keyword")] string keyword,
        [Description("Maximum results to return")] int maxResults = 5)
    {
        // Query Umbraco content
        return $"Found {maxResults} items of type '{contentType}' matching '{keyword}'.";
    }
}
```

{% endcode %}

{% hint style="info" %}
Tool support varies by provider and model. Most modern models (GPT-4o, Claude 3.5, Gemini 1.5) support tools. Check your provider documentation for details.
{% endhint %}

## Related

* [Tools Overview](/ai-in-umbraco/19.latest/using-the-api/tools.md) - Tools concepts
* [Basic Chat](/ai-in-umbraco/19.latest/using-the-api/chat/basic-chat.md) - Chat without tools
* [Creating Custom Tools](/ai-in-umbraco/19.latest/extending/tools/creating-a-tool.md) - The `AIToolBase` pattern for agent tools


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.umbraco.com/ai-in-umbraco/19.latest/using-the-api/tools/using-tools.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
