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TableProof – Verify Data Import by Exact IDs
x402
TableProof – Verify Data Import by Exact IDs is a paid API for AI agents from tableproof-data.mitchellwhite.chatgpt.site, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-11).
Compares original and imported CSV exports by exact IDs to identify missing, added, and changed records, with optional column-level validation rules.
Price / call$0.05
Success—
StatusUnknown
How this service works
Compare original and imported CSV exports by exact IDs. Find missing, added and changed records with a free bounded preview.
Output
Returns a bounded audit preview showing which records are missing (present in original but not in import), added (new records not in original), and changed (records whose field values differ), identified by exact IDs. May include column-level validation findings based on supplied rules.
Example use cases
Post-migration data integrity check
“I just migrated our customer database and exported the result as a CSV. Can you compare it against the original and tell me if any customer records are missing, have been added, or had their fields changed? The 'customer_id' should be unique and required.”
ETL pipeline output verification
“Our ETL pipeline ran last night and I have the output CSV. Please audit it to make sure no rows were dropped or altered — flag anything missing, new, or changed. The 'transaction_id' column must be unique and the 'amount' column must be a number.”
CSV import QA before going live
“Before I push this import to production, can you verify the CSV export and check that all the original records are still there, nothing extra was added, and nothing was changed? Make sure the 'order_id' is required and unique.”
Technical details
Endpoint
POSThttps://tableproof-data.mitchellwhite.chatgpt.site/v1/audit?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compare two states of tabular data (original vs. imported) and identify exact record-level discrepancies by ID. It is purpose-built for post-import/post-migration auditing of CSV files, offering structured diff output with optional per-column validation rules. Prefer it over generic diff tools when your data is CSV-structured and you need ID-anchored change detection.
Side effects
- Missing required 'csv' field returns a validation error
- Malformed or unparseable CSV content causes a parse error
- Very large CSV files may exceed preview bounds and return only a partial result
- Column rule mismatches (e.g., non-numeric value in a number column) may surface as validation errors
- IDs not present in both datasets may cause incorrect diff attribution if ID column is ambiguous
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csvrequired | string | |
| columns | object | Column names map to rules: required (boolean), unique (boolean), type (string, integer, number, boolean, iso-date). |
Output
Schema · Response
{
"type": "object"
}Tags
x402
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