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SignalHarness LLM Eval Result Aggregator
x402
SignalHarness LLM Eval Result Aggregator is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-15).
Aggregates and summarizes evaluation results from LLM outputs, returning structured analysis JSON with warnings and evidence scope metadata.
Price / call$0.01
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing a 'result' field with 'analysis_json' (structured aggregate analysis as a JSON string), 'warnings' (array of any data quality or trust cautions), 'service_id', and 'evidence_scope'. Also includes a 'status' field ('succeeded'/'failed'), full payment receipt with Base USDC transaction details, and request/execution IDs for traceability.
Example use cases
Batch LLM benchmark rollup
“I ran evaluations on three different LLM responses for the same prompt and got separate scores — can you aggregate this JSON into a single consolidated result with any data quality warnings? Here's the eval data: {"runs":[{"score":0.88,"model":"claude-3"},{"score":0.75,"model":"gpt-4"},{"score":0.91,"model":"gemini"}]}.”
Model quality monitoring pipeline
“As part of my nightly eval pipeline, aggregate these LLM evaluation results from today's runs and give me back a structured analysis JSON I can store — flag anything suspicious in the warnings field. Input: {"date":"2025-01-15","evals":[{"task":"rag","score":0.84},{"task":"summarization","score":0.79}]}.”
A/B test result consolidation
“I'm comparing two fine-tuned models and have evaluation JSON for both — please aggregate the results and tell me what the combined analysis looks like, including the evidence scope so I know how much to trust it. Data: {"model_a":{"f1":0.88},"model_b":{"f1":0.91},"test":"classification"}.”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/llm_eval_result_aggregate/invokeWhen to prefer
Choose this endpoint when you need a pay-per-call, agent-native service to aggregate and structure LLM evaluation results without standing up your own eval infrastructure. Best suited for pipelines where you have raw evaluation JSON and need a structured, evidence-scoped aggregate analysis returned instantly. Prefer this over DIY aggregation when you need built-in receipt/audit trails (Base USDC, SHA256 result hash) for accountability in agentic workflows.
Side effects
- Malformed or non-JSON request_json input causes validation failure
- request_json exceeds 65536 character limit returns error
- Empty or minimal JSON (under 2 characters) rejected by minLength constraint
- Payment failure on Base network prevents execution
- Caller-supplied data flagged with trust warnings in result — data not independently verified by the service
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "llm_eval_result_aggregate",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "llm_eval_result_aggregate",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "10000",
"resultSha256": "5696e63c3b286d34c9110108be3b9a1437ddca4931ef8f7b1551b38e5e427c78",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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