Result Fields

Updated April 24, 2026 16:12

Use the following API methods to request details about system and custom fields for test results.

get_result_fields

Returns a list of available test result custom fields.

GET index.php?/api/v2/get_result_fields

Response content

The response includes an array of custom field definitions. Please see below for a typical response:

[
    {
        "id": 19,
        "is_active": true,
        "type_id": 3,
        "name": "ai_input",
        "system_name": "custom_ai_input",
        "label": "Input",
        "description": "LLM input for AI evaluation",
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": []
                },
                "options": {
                    "is_required": false,
                    "default_value": "",
                    "format": "markdown",
                    "rows": "10"
                },
                "id": "8f3e4d5c-9a7b-4e1f-8c2d-6a5b4e3f2d1c"
            }
        ],
        "display_order": 102,
        "include_all": false,
        "i18n_custom_id": "fields_custom_ai_input",
        "template_ids": [
            5
        ]
    },
    {
        "id": 22,
        "is_active": true,
        "type_id": 1,
        "name": "ai_latency",
        "system_name": "custom_ai_latency",
        "label": "Latency",
        "description": "Response latency for AI evaluation",
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": []
                },
                "options": {
                    "is_required": false,
                    "default_value": "",
                    "format": "markdown",
                    "rows": "10"
                },
                "id": "5d4c3b2a-6e7f-4d8e-9a7c-3b2e1d9c8f7a"
            }
        ],
        "display_order": 105,
        "include_all": false,
        "i18n_custom_id": "fields_custom_ai_latency",
        "template_ids": [
            5
        ]
    },
    {
        "id": 20,
        "is_active": true,
        "type_id": 3,
        "name": "ai_output",
        "system_name": "custom_ai_output",
        "label": "Output",
        "description": "LLM output for AI evaluation",
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": []
                },
                "options": {
                    "is_required": false,
                    "default_value": "",
                    "format": "markdown",
                    "rows": "10"
                },
                "id": "7a2b9c4e-8d6f-4a3e-9b1c-5e4d3a2b1c9f"
            }
        ],
        "display_order": 103,
        "include_all": false,
        "i18n_custom_id": "fields_custom_ai_output",
        "template_ids": [
            5
        ]
    },
    {
        "id": 21,
        "is_active": true,
        "type_id": 4,
        "name": "ai_traces",
        "system_name": "custom_ai_traces",
        "label": "Traces (URL)",
        "description": "Trace URL for AI evaluation",
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": []
                },
                "options": {
                    "is_required": false,
                    "default_value": ""
                },
                "id": "6e5d4c3b-7a8f-4e9d-8c6b-4a3e2d1c9b8f"
            }
        ],
        "display_order": 104,
        "include_all": false,
        "i18n_custom_id": "fields_custom_ai_traces",
        "template_ids": [
            5
        ]
    },
    {
        "id": 11,
        "is_active": true,
        "type_id": 11,
        "name": "step_results",
        "system_name": "custom_step_results",
        "label": "Steps",
        "description": null,
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": null
                },
                "options": {
                    "is_required": false,
                    "format": "markdown",
                    "has_expected": true,
                    "has_actual": true,
                    "rows": "5"
                },
                "id": "4be97c65ea2fd"
            }
        ],
        "display_order": 1,
        "include_all": false,
        "i18n_custom_id": "fields_custom_step_results",
        "template_ids": [
            2
        ]
    },
    {
        "id": 16,
        "is_active": true,
        "type_id": 14,
        "name": "testrail_bdd_scenario_results",
        "system_name": "custom_testrail_bdd_scenario_results",
        "label": "BDD Scenario Results",
        "description": null,
        "configs": [
            {
                "context": {
                    "is_global": true,
                    "project_ids": []
                },
                "options": {
                    "is_required": false,
                    "format": "markdown",
                    "rows": "6"
                },
                "id": "b8ec6053-7705-40a4-91c7-b0405e42cfce"
            }
        ],
        "display_order": 1,
        "include_all": false,
        "i18n_custom_id": "fields_custom_testrail_bdd_scenario_results",
        "template_ids": [
            4
        ]
    }
]

A custom field can have different configurations and options per project which is indicated by the configs field. To check if a custom field is applicable to a specific project (and to find out the field options for this project), the context of the field, the configuration must either be global (is_global) or include the ID of the project in project_ids.

Also, the following list shows the available custom field types (type_id field):

Type ID	Name
1	String
2	Integer
3	Text
4	URL
5	Checkbox
6	Dropdown
7	User
8	Date
9	Milestone
11	Step Results
12	Multi-select
13	Scenarios
14	Scenario Results
15	AI Automation
16	Rating

Response codes

Status Code	Description
200	Success (the available custom fields are returned as part of the response)

quality_rating

Represents a structured evaluation of a test result or output across multiple quality dimensions.

This field is available in TestRail 10.3 or later.

While commonly used in AI evaluation workflows, this field is not limited to AI. It can also be used to assess outcomes in:

Performance testing (e.g. efficiency, stability, responsiveness)
Security testing (e.g. compliance, risk exposure, robustness)
Functional testing (e.g. completeness, correctness)

This makes it a flexible scoring mechanism for both automated and manual evaluation processes.

The field may be:

System-generated (e.g. by AI models, analysis tools, or pipelines), or
User-submitted as part of structured test evaluations

Value range

Typically integer scores (e.g. 1–5)
Range may be configurable depending on implementation

Example

{
  "quality_rating": {
    "factual_accuracy": 4,
    "relevance": 5,
    "completeness": 3,
    "clarity_structure": 4,
    "safety_compliance": 5,
    "actionability": 4
  }
}

When to use

Use quality_rating when you need structured evaluation across dimensions, such as:

Comparing outputs from different systems or models
Scoring test results beyond pass/fail
Evaluating AI-generated artefacts
Measuring performance or security outcomes
Tracking quality trends over time

Notes

Not a standard TestRail system field; part of extended or custom implementations
Typically used alongside:
- custom_ai_input
- custom_ai_output
- custom_ai_latency
- custom_ai_traces

quality_rating extends traditional pass/fail results by enabling multi-dimensional scoring of outcomes, supporting AI evaluation, performance analysis, and security validation.