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CSV Import Rehearsal Analyzer
x402
CSV Import Rehearsal Analyzer is a paid API for AI agents from mcp-factory.bowling-anthony.workers.dev, paid per call via x402, $0.03/call, status unknown (last checked 2026-09-15).
Validates and analyzes CSV data rows for import readiness, returning per-row errors, accepted/rejected status, normalized corrections, and a summary.
Price / call$0.03
Success—
StatusUnknown
How this service works
Customer-input analysis tools with per-call x402 v2 USDC payments on Base. Discovery and examples are free.
Output
A JSON object with per-row results (index, accepted boolean, parsed values, and a list of errors with code, line, column, and message), plus a headers array, a summary (total rows, accepted count, rejected count), and a corrections array detailing each normalization applied (before/after values, column, and reason).
Example use cases
Pre-import CSV data audit
“Before I bulk-load this customer CSV into our database, can you run it through the import rehearsal and show me which rows have errors, which are clean, and what got auto-corrected? Here's the CSV: id,name,when,active,amount\n1,Alice,2024-03-15,true,99.99\n2,,bad-date,false,0”
Detecting required-field gaps in uploads
“I'm about to import a batch of user records from this CSV but I'm worried some rows are missing required fields like name or date — can you analyze it row by row and flag any required_missing or type_mismatch errors before I commit the import?”
Data normalization review for finance team
“Our finance team uploaded a transaction CSV and some of the amount columns look weirdly formatted — can you run the import rehearsal on it and tell me what normalizations like decimal fixes or whitespace trims were applied before we accept those rows?”
Technical details
Endpoint
POSThttps://mcp-factory.bowling-anthony.workers.dev/csv-import-rehearsal/analyzeWhen to prefer
Choose this endpoint when you need a structured, row-level validation report before committing a CSV to a real import — especially when you want to identify type mismatches, missing required fields, and applied normalizations in a single pass. It is ideal for agent-driven ETL pipelines, customer-data onboarding flows, or any workflow where you want a rehearsal/dry-run before writing data to a database.
Side effects
- Malformed CSV input with no parseable headers returns an error response
- Rows with ambiguous date formats may produce type_mismatch errors even for borderline-valid inputs
- Payment failure (insufficient USDC balance on Base) results in a 402 response blocking execution
- Very large CSVs may time out if the Cloudflare Worker execution limit is hit
- Missing required columns in the schema definition may cause all rows to be rejected
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"rows": [
{
"index": 1,
"errors": [],
"values": {
"id": "1",
"name": "Doe, Jane",
"when": "2024-02-29",
"active": true,
"amount": "12.50"
},
"accepted": true
},
{
"index": 2,
"errors": [
{
"code": "required_missing",
"line": 3,
"column": "name",
"message": "required value is blank"
},
{
"code": "type_mismatch",
"line": 3,
"column": "when",
"message": "expected a valid YYYY-MM-DD date"
}
],
"values": {
"id": "2",
"name": null,
"when": null,
"active": false,
"amount": "-3"
},
"accepted": false
}
],
"headers": [
"id",
"name",
"amount",
"active",
"when"
],
"summary": {
"rows": 2,
"accepted": 1,
"rejected": 1
},
"corrections": [
{
"row": 1,
"after": "12.50",
"before": "012.50",
"column": "amount",
"reason": "normalized decimal format"
},
{
"row": 2,
"after": "",
"before": " ",
"column": "name",
"reason": "trimmed whitespace"
}
],
"formula_cells": []
}
}Tags
x402
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