Tools

Define custom functions and API integrations your agents can invoke during calls.

Tools

Tools extend your agent’s capabilities beyond conversation. They allow the agent to perform real actions during a call — like booking appointments, looking up customer records, or triggering webhooks.

How Tools Work

1. You define a tool with a name, description, and expected parameters
2. The tool is attached to an agent
3. During a call, the LLM decides when to invoke a tool based on the conversation context
4. The tool executes (API call, webhook, etc.) and the result is fed back to the agent
5. The agent uses the result to continue the conversation

This is also known as function calling — a capability of modern LLMs.

Creating a Tool

1. Navigate to Build > Tools
2. Click New Tool
3. Configure:
   • Name: a clear function name (e.g. check_availability)
   • Description: explain what this tool does — the LLM reads this to decide when to use it
   • Parameters: define the inputs the tool expects (JSON Schema format)
   • Endpoint: the URL to call when the tool is invoked

Tool Configuration

Field	Description
Name	Function name the LLM will reference
Description	When and why to use this tool (crucial for accuracy)
Parameters	JSON Schema defining expected inputs
Endpoint URL	HTTP endpoint called with the parameters
Method	HTTP method (GET, POST, etc.)
Headers	Custom headers (e.g. API keys)

Example: Appointment Booking Tool

{
  "name": "book_appointment",
  "description": "Book an appointment for the caller. Use when the caller explicitly requests to schedule a visit.",
  "parameters": {
    "type": "object",
    "properties": {
      "caller_name": {
        "type": "string",
        "description": "Full name of the caller"
      },
      "date": {
        "type": "string",
        "description": "Requested date in YYYY-MM-DD format"
      },
      "time": {
        "type": "string",
        "description": "Requested time in HH:MM format"
      }
    },
    "required": ["caller_name", "date", "time"]
  }
}

Best Practices

Write precise descriptions

The LLM uses the tool description to decide when to call it. A vague description leads to incorrect or missed invocations. Be specific about trigger conditions.

Handle errors gracefully

Your API endpoint should return clear error messages. The agent will relay these to the caller, so make them human-readable.

Keep tools focused

One tool = one action. Don’t create a “do everything” tool. Split complex workflows into multiple tools with clear responsibilities.

Test with real scenarios

Call your agent and try to trigger each tool. Verify the parameters are passed correctly and the response is used appropriately.