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Perception tool calling works with OpenAI’s Function Calling and can be configured in the perception layer. It allows an AI agent to trigger functions based on visual cues during a conversation.
The perception layer tool calling is only available for raven-0.

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Defining Tool

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Top-Level Fields

FieldTypeRequiredDescription
typestring✅Must be "function" to enable tool calling.
functionobject✅Defines the function that can be called by the model. Contains metadata and a strict schema for arguments.

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function

FieldTypeRequiredDescription
namestring✅A unique identifier for the function. Must be in snake_case. The model uses this to refer to the function when calling it.
descriptionstring✅A natural language explanation of what the function does. Helps the perception model decide when to call it.
parametersobject✅A JSON Schema object that describes the expected structure of the function’s input arguments.

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function.parameters

FieldTypeRequiredDescription
typestring✅Always "object". Indicates the expected input is a structured object.
propertiesobject✅Defines each expected parameter and its corresponding type, constraints, and description.
requiredarray of strings✅Specifies which parameters are mandatory for the function to execute.
Each parameter should be included in the required list, even if they might seem optional in your code.
function.parameters.properties
Each key inside properties defines a single parameter the model must supply when calling the function.
FieldTypeRequiredDescription
<parameter_name>object✅Each key is a named parameter. The value is a schema for that parameter.
Optional subfields for each parameter:
SubfieldTypeRequiredDescription
typestring✅Data type (e.g., string, number, boolean).
descriptionstring❌Explains what the parameter represents and how it should be used.
enumarray❌Defines a strict list of allowed values for this parameter. Useful for categorical choices.

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Example Configuration

Here’s an example of tool calling in perception layers:
Best Practices:
  • Use clear, specific function names to reduce ambiguity.
  • Add detailed description fields to improve selection accuracy.
Perception Layer
"perception": {
  "perception_model": "raven-0",
  "ambient_awareness_queries": [
      "Is the user showing an ID card?",
      "Is the user wearing a mask?"
  ],
  "perception_tool_prompt": "You have a tool to notify the system when an ID card is detected, named `notify_if_id_shown`.",
  "perception_tools": [
    {
      "type": "function",
      "function": {
        "name": "notify_if_id_shown",
        "description": "Use this function when a drivers license or passport is detected in the image with high confidence. After collecting the ID, internally use final_ask()",
        "parameters": {
          "type": "object",
          "properties": {
            "id_type": {
              "type": "string",
              "description": "best guess on what type of ID it is",
            },
          },
          "required": ["id_type"],
        },
      },
    },
    {
      "type": "function",
      "function": {
        "name": "notify_if_bright_outfit_shown",
        "description": "Use this function when a bright outfit is detected in the image with high confidence",
        "parameters": {
          "type": "object",
          "properties": {
            "outfit_color": {
              "type": "string",
              "description": "Best guess on what color of outfit it is"
            }
          },
          "required": ["outfit_color"]
        }
      }
    }
  ]
}

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How Perception Tool Calling Works

Perception Tool calling is triggered during an active conversation when the perception model detects a visual cue that matches a defined function. Here’s how the process works:
This example explains the notify_if_id_shown function from the example configuration above.
The same process applies to other functions like notify_if_bright_outfit_shown, which is triggered if a bright-colored outfit is visually detected.

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Modify Existing Tools

You can update the perception_tools definitions using the Update Persona API. 
curl --request PATCH \
  --url https://tavusapi.com/v2/personas/{persona_id} \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '[
    {
      "op": "replace",
      "path": "/layers/perception/perception_tools",
      "value": [
        {
          "type": "function",
          "function": {
            "name": "detect_glasses",
            "description": "Trigger this function if the user is wearing glasses in the image",
            "parameters": {
              "type": "object",
              "properties": {
                "glasses_type": {
                  "type": "string",
                  "description": "Best guess on the type of glasses (e.g., reading, sunglasses)"
                }
              },
              "required": ["glasses_type"]
            }
          }
        }
      ]
    }
  ]'
Replace <api_key> with your actual API key. You can generate one in the Developer Portal.
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