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CSV Drift Evidence
x402
CSV Drift Evidence is a paid API for AI agents from foundry-csv-drift-evidence-mainnet.inference-chip-index.workers.dev, paid per call via x402, $0.015/call, status unknown (last checked 2026-09-15).
Compares a candidate CSV against a baseline CSV and returns deterministic structural drift evidence including column, type, and missing-rate changes.
Price / call$0.015
Success—
StatusUnknown
How this service works
Deterministically compares a candidate CSV with a known-good baseline and returns compact schema, missingness, type, order, and numeric-range drift evidence. Results are decision support, not a guarantee of downstream safety.
Output
Returns deterministic structural drift evidence including: lists of added, removed, or renamed columns; type changes per column; missing-rate deltas that exceed the configured review threshold; and numeric range violations for specified columns compared to the baseline.
Example use cases
Data pipeline output validation
“I have my Friday pipeline output CSV and the reference baseline from last Monday — can you check whether any columns were dropped or added, whether missing rates jumped by more than 5%, and flag if 'revenue' or 'units_sold' went outside their historical ranges?”
ETL schema drift monitoring
“Before we load this new vendor data CSV into the warehouse, compare it against our approved baseline and tell me about any column renames, new fields, or type changes — treat 'vendor_segment' as a new column we're expecting.”
Data quality audit before ML training
“I need to check whether our training dataset this month has drifted structurally from last month's verified CSV — look for missing rate changes, dropped features, and check that 'age', 'income', and 'score' stay within their known ranges.”
Technical details
Endpoint
POSThttps://foundry-csv-drift-evidence-mainnet.inference-chip-index.workers.dev/v1/csv/driftWhen to prefer
Choose this endpoint when you need deterministic, reproducible structural drift detection between two CSV snapshots — particularly for data pipeline validation, ETL monitoring, or pre-ingestion checks. It is ideal when you want explicit, auditable evidence of schema changes rather than statistical or ML-based drift scores. Prefer it over general data profiling tools when the focus is on column-level structure, missing rates, and numeric range adherence against a known-good baseline.
Side effects
- Malformed or empty CSV strings cause validation errors
- CSV exceeding 250,000 characters is rejected
- Column names in numericRangeColumns not present in baseline return warnings or are skipped
- Mismatched encodings or line endings may cause parsing failures
- Extremely wide CSVs with hundreds of columns may hit item limits on allowedNewColumns or numericRangeColumns
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| baselineCsv | string | Known-good CSV including one header row. |
| candidateCsv | string | Candidate CSV to compare with the baseline. |
| allowedNewColumns | array | New candidate columns that should not reduce the score. |
| numericRangeColumns | array | Baseline numeric columns whose observed range should be checked. |
| missingRateReviewDelta | number | Absolute missing-rate change that is reported for review. |
Output
Schema · Response
{
"type": "json",
"example": {
"score": 88,
"product": "csv-drift-evidence",
"version": "1.0.0",
"disposition": "REVIEW",
"typeChanges": [],
"addedColumns": [
"source"
],
"baselineRows": 2,
"candidateRows": 2,
"removedColumns": [],
"reorderedColumns": false,
"missingRateChanges": [
{
"delta": 0.5,
"column": "region",
"baseline": 0,
"candidate": 0.5
}
],
"evidenceFingerprint": "15a1c350fa3d73358e9e0d846942c90e31a8923cbf6f41bcb2c10030505357a3",
"disallowedAddedColumns": [],
"numericRangeExcursions": [
{
"above": 1,
"below": 0,
"column": "score",
"baselineMaximum": 0.9,
"baselineMinimum": 0.7
}
]
}
}Tags
x402
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