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CSV Profiler – Shape, Types & Quality Analysis
x402
CSV Profiler – Shape, Types & Quality Analysis is a paid API for AI agents from foundry-par007-machine-revenue-mainnet.inference-chip-index.workers.dev, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-14).
Profiles a caller-supplied CSV payload for row count, column count, inferred data types, missing values, uniqueness, and inconsistent row-width issues.
Price / call$0.01
Success—
StatusUnknown
How this service works
Pay-per-request evidence and data tools: CSV validation and reconciliation, DNS/email configuration evidence, and bounded official weather, earthquake, vehicle, company and study records. Exact USDC prices, explicit JSON contracts and source provenance. No mailbox, ownership, medical or safety guarantees.
Output
Returns a JSON object with row_count, column_count, inconsistent_row_count, per-column inferred types, missing value counts, and uniqueness metrics for each column in the supplied CSV.
Example use cases
Pre-import data quality check
“Before I load this CSV into our database, can you profile it and tell me if there are missing values, inconsistent rows, or columns with unexpected types?”
Exploratory data summary for analysts
“I've got a CSV export from our CRM — can you give me a quick shape summary: how many rows and columns, what types are inferred for each column, and are there any uniqueness issues I should know about?”
Pipeline validation before ETL
“Run a data profile on this CSV and flag any rows with inconsistent widths or columns that have a high percentage of missing values — I need to know before kicking off the ETL job.”
Technical details
Endpoint
POSThttps://foundry-par007-machine-revenue-mainnet.inference-chip-index.workers.dev/v1/csv/profileWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call CSV profiling step before ingesting, transforming, or storing tabular data. It is well-suited for agent pipelines that need to validate CSV structure and quality without standing up a dedicated data profiling service. Prefer it over manual inspection or heavier ETL tools when a fast, structured quality summary is needed on a bounded (size-limited) CSV.
Side effects
- CSV exceeds size or row bounds — returns error indicating payload is too large
- Malformed CSV that cannot be parsed — returns parse error with line indication
- Empty CSV with no rows — may return zeroed metrics or an error
- Missing required CSV body in POST request — returns 400 bad request
- Network timeout if CSV processing takes too long — returns timeout error
Output
Schema · Response
{
"type": "json",
"example": {
"result": {
"columns": [
{
"name": "name",
"examples": [
"Ada",
"Lin"
],
"max_length": 3,
"min_length": 3,
"numeric_max": null,
"numeric_min": null,
"unique_count": 2,
"inferred_type": "string",
"null_or_empty_count": 0,
"duplicate_nonempty_count": 0
},
{
"name": "score",
"examples": [
"10"
],
"max_length": 2,
"min_length": 2,
"numeric_max": 10,
"numeric_min": 10,
"unique_count": 1,
"inferred_type": "integer",
"null_or_empty_count": 1,
"duplicate_nonempty_count": 0
}
],
"headers": [
"name",
"score"
],
"delimiter": ",",
"row_count": 2,
"has_header": true,
"column_count": 2,
"input_sha256": "8d2f18a153292a78df165121ca2b8fb9989a2329356ae5e3ccd3749a8b1b3b70",
"duplicate_headers": [],
"inconsistent_rows": [],
"inconsistent_row_count": 0
},
"service": "Foundry PAR-007",
"version": "illustrative",
"endpoint": "csv-quality-profile",
"protocol": "x402-v2"
}
}Tags
x402
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