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Hermes Agent Commerce API – Profile (Data Quality)
x402
Hermes Agent Commerce API – Profile (Data Quality) is a paid API for AI agents from hermes-counterparty-api.onrender.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-14).
Analyzes JSON or CSV records and returns a deterministic data-quality score with field-level diagnostics and warnings.
Price / call$0.02
Success—
StatusUnknown
How this service works
Agent utilities for company research, package maintenance intelligence, dependency vulnerability checks, counterparty availability, SEC company snapshots, OFAC sanctions screening, and deterministic JSON/CSV data-quality work. Company domain intelligence includes a free preview before the paid enrichment call.
Output
Returns a JSON object containing a numeric quality_score (0–100), field-level diagnostics keyed by field name, dataset summary (field_count, record_count), a list of warnings, processing time in milliseconds, and schema/scoring version strings.
Example use cases
Pre-pipeline dataset audit
“Before I load these JSON records into our data warehouse, can you profile them and tell me the quality score, which fields have issues, and any warnings I should know about?”
CSV data validation for reporting
“I have a CSV export with about 500 rows — can you score the data quality and flag any fields that look malformed or incomplete before I use it in the report?”
Agent data hygiene check
“My agent just collected a batch of JSON objects from multiple sources — can you run a deterministic quality profile on them and tell me the field count, record count, and any warnings before we proceed?”
Technical details
Endpoint
POSThttps://hermes-counterparty-api.onrender.com/v1/profileWhen to prefer
Choose this endpoint when you need a deterministic, reproducible data-quality score on JSON or CSV records before ingestion, transformation, or reporting — especially in agent pipelines where auditability and consistent scoring versions matter. Prefer it over ad-hoc LLM-based data review when you need a numeric score and structured field-level warnings rather than a free-text assessment.
Side effects
- Missing required 'input' object returns a validation error
- Mismatched format and body type (e.g. format=csv but body is a JSON array) causes a schema rejection
- Empty records array may return a zero-record dataset with no meaningful score
- Large payloads may time out on the Render.com free-tier host
- Payment not included or insufficient USDC triggers an HTTP 402 response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"fields": {},
"dataset": {
"field_count": 5,
"record_count": 100
},
"warnings": [],
"processing_ms": 42,
"quality_score": 85,
"schema_version": "1.0",
"scoring_version": "1.0"
}
}Tags
x402
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