PATCH /api/v2/conversations/calls/{id}/participants/{id} 400: Invalid participant attributes payload

NICE CXone API & Integrations
1
  • Genesys Cloud API v2
  • Python SDK 2.0.14
  • Kafka Connect source connector reading from EventBridge

PATCH /api/v2/conversations/calls/{conversationId}/participants/{participantId}
400 Bad Request: The request body contains invalid values.

I am pushing external CRM attributes back to the call participant record via a Kafka sink. The JSON payload follows the ParticipantAttributes schema, but the API rejects it. Can anyone clarify why the API returns a 400 when the attributes object is not empty?

{
 "attributes": {
 "crm_id": "12345",
 "vip_status": "true"
 }
}
2

You are likely sending nulls or empty strings for required fields; the API is strict about omitting keys you don’t want to change.

# Only include fields you intend to update
payload = {"wrapup_code": {"id": "new-wrapup-id"}}
# Ensure no None values are serialized

Note: Check the application/json header and ensure you are not sending nested objects that don’t exist in the schema.

2 Likes
3

This is actually a known issue…

PATCH /api/v2/conversations/calls/{conversationId}/participants/{participantId}
400 Bad Request: The request body contains invalid values.

The suggestion above regarding null values is partially correct, but the root cause is usually stricter schema validation on the ParticipantAttributes object when sent via PATCH. The API expects a sparse object containing only the specific attributes you wish to update, not a full representation of the participant object. If your Kafka sink serializes the entire participant record, including read-only fields or fields with null values, the 400 error is guaranteed.

For sentiment analysis pipelines, I recommend isolating the attribute update to a minimal payload. Here is a robust Python snippet using the genesyscloud SDK that ensures only non-null, valid attributes are serialized:

from genesyscloud.rest import ApiException
import json

def update_participant_attrs(api_instance, conv_id, part_id, custom_attrs):
 # Filter out None values explicitly to avoid schema rejection
 clean_attrs = {k: v for k, v in custom_attrs.items() if v is not None}
 
 # Construct the specific patch payload structure
 # Note: The SDK expects a Participant object, but for attributes, 
 # we often need to manually construct the JSON to avoid SDK serialization quirks
 patch_payload = {
 "attributes": clean_attrs
 }
 
 try:
 # Use the low-level API call to bypass SDK object validation if needed
 api_response = api_instance.patch_conversation_call_participant(
 conversation_id=conv_id,
 participant_id=part_id,
 body=patch_payload
 )
 return api_response
 except ApiException as e:
 print(f"Exception: {e.body}")
 return None

Ensure your Kafka consumer maps the CRM data directly to custom_attrs without wrapping it in unnecessary nested objects. The attributes key is mandatory for this endpoint.

1 Like
4 
# Avoid sending full ParticipantAttributes; use sparse payload
payload = {"wrapup_code": {"id": "new-wrapup-id"}}

Have you tried stripping all nulls before serialization? The API rejects strict schema mismatches, so ensure your Pydantic model uses exclude_none=True to avoid sending empty strings or nulls for untouched fields.