from meibel import MeibelClient
client = MeibelClient(api_key="your-api-key")
result = client.confidence_scoring.get_scoring_job("job_id_value")
print(result){
"job_id": "<string>",
"agent_identity_context": {
"customer_id": "<string>",
"project_id": "<string>",
"agent_name": "<string>",
"agent_version": "<string>",
"agent_session_id": "<string>",
"agent_workflow_name": "<string>",
"agent_workflow_version": "<string>",
"agent_workflow_session_id": "<string>",
"tool_id": "<string>",
"tool_instance_id": "<string>",
"tool_execution_id": "<string>"
},
"module": "<string>",
"input_value": "<string>",
"output_value": "<string>",
"status": "<string>",
"scoring_config": "<unknown>",
"score": 123
}Retrieve a single confidence scoring job by its ID, including its current status and score if completed.
from meibel import MeibelClient
client = MeibelClient(api_key="your-api-key")
result = client.confidence_scoring.get_scoring_job("job_id_value")
print(result){
"job_id": "<string>",
"agent_identity_context": {
"customer_id": "<string>",
"project_id": "<string>",
"agent_name": "<string>",
"agent_version": "<string>",
"agent_session_id": "<string>",
"agent_workflow_name": "<string>",
"agent_workflow_version": "<string>",
"agent_workflow_session_id": "<string>",
"tool_id": "<string>",
"tool_instance_id": "<string>",
"tool_execution_id": "<string>"
},
"module": "<string>",
"input_value": "<string>",
"output_value": "<string>",
"status": "<string>",
"scoring_config": "<unknown>",
"score": 123
}Documentation Index
Fetch the complete documentation index at: https://docs.meibel.ai/llms.txt
Use this file to discover all available pages before exploring further.
Unique identifier of the scoring job to retrieve.
Successful Response
A single confidence scoring job and its result.
Unique identifier for this scoring job.
The agent, workflow, and tool context that produced the scored output.
Show child attributes
The scoring module used to evaluate the output. Judge-based modules (e.g. correctness, coherence, faithfulness) produce scores on a 0–10 scale. Statistical modules (e.g. observed_consistency, data_grounding) produce scores on a 0.0–1.0 scale.
The input that was provided to the agent or tool being scored.
The output produced by the agent or tool that was evaluated.
Current status of the scoring job: submitted, in_progress, completed, failed, or not_run.
Configuration parameters for the scoring module. Structure varies by module.
The computed confidence score, or null if the job has not completed. Range depends on the module: 0–10 (integer) for judge-based modules, 0.0–1.0 for statistical modules.
Was this page helpful?