> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getbutter.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Tools

> Connecting agents to external APIs

Tools (function calling) allow agents to perform actions beyond just talking. This turns a conversational bot into a capable assistant.

## Tool Configuration

A tool consists of:

1. **Name**: Unique identifier (e.g., `check_inventory`).
2. **Description**: Natural language explanation of *when* the LLM should use this tool (e.g., "Use this to check if an item is in stock").
3. **API Config**:
   * **URL**: The endpoint to call.
   * **Method**: GET, POST, PUT, DELETE.
   * **Headers**: Auth tokens or content types.
   * **Parameters**: JSON Schema defining the data the LLM needs to extract from the user.

## Example

**Scenario**: A user asks, "Do you have any red sneakers in size 10?"

**Tool Config**:

```json theme={null}
{
  "name": "check_inventory",
  "description": "Checks stock availability",
  "parameters": {
    "type": "object",
    "properties": {
      "item": {"type": "string"},
      "color": {"type": "string"},
      "size": {"type": "integer"}
    }
  }
}
```

**Flow**:

1. LLM analyzes user speech.
2. LLM decides to call `check_inventory` with `{"item": "sneakers", "color": "red", "size": 10}`.
3. Butter AI executes the HTTP request to your backend.
4. Your backend returns `{"available": true, "quantity": 5}`.
5. LLM receives this data and says: "Yes, we have 5 pairs in stock."

## Creating Tools

You can create tools via the API and then add the `tool_id` to your agent's `custom_tools` list.