Import Check: Validate & Clean CSV

Reviewed 1d ago.

• tools: 10 tools scanned
• metadata: scanned

No findings.

Tools (10)

• get_csv_example

  Return a fixed synthetic CSV and its column schema. Free, no key or quota. Copy the structure, then supply your own CSV to validate_csv.

• view_csv_demo

  Validate the fixed example with the current engine. Free and no arguments; demonstrates duplicate, email, amount and date findings. Custom files require validate_csv and paid access.

• get_csv_usage

  Read remaining operations and expiry for the API key supplied in the Authorization header. Does not consume credit.

• get_table_example

  Return fixed synthetic left/right tables and a reusable join recipe. No arguments, key or credit. Copy the structure for reconcile_tables; mode controls which unmatched rows appear in output.

• view_table_demo

  Join the bundled fixed tables and show changed and unmatched records. No arguments, key or credit. This cannot process custom data; use reconcile_tables with a paid key for your files.

• inspect_table

  Profile a CSV or explicit string table before writing a recipe. Return empty, distinct and duplicate-value counts per column and complete formula warnings; no schema or business validity is inferred. Supply CSV OR columns/rows, never both. JSON numbers must be encoded as strings so identifiers keep leading zeros. One paid operation; no content retained. Reuse request_id only for identical input.

• prepare_table

  Select, rename, derive and convert columns using a caller-owned version-1 recipe. Rules reference original input headers; output follows recipe order and omitted columns are listed. All input rows remain. Any failed conversion returns valid=false, all findings and csv_text=null, never a partially transformed file. No arithmetic, arbitrary code, schema inference or destination writes. One completed report costs one operation, including valid=false. Use reconcile_tables for two-file matching.

• reconcile_tables

  Join two bounded tables using exact string or composite keys. Both sides must have non-empty unique keys: duplicates block the entire output, including many-to-many matches. Choose left, inner or full retention explicitly; unmatched records are reported even if excluded. Optional compare fields flag changed values without resolving them. Output includes row lineage; missing-side fields are empty strings. No trimming, fuzzy matching or destination writes. Combined inputs share 1 MB/10,000 records/250,000 cells. One completed report costs one operation; reuse request_id for identical retries. Full findings or an uncharged size error, never silent truncation.

• validate_csv

  Check a CSV against your explicit schema; return all issue counts and at most 200 detailed findings. Headers match exactly; required=false permits empty cells, not missing headers. Unique compares raw non-empty strings. Dates use YYYY-MM-DD, decimals use a dot without grouping, booleans use true/false. A valid=false report is a completed paid operation, not a tool failure. No values are changed; use clean_csv for explicit transformations. Validity covers only the supplied rules, not destination-system or business correctness.

• clean_csv

  Apply only requested column renames and transformations, then validate the output against schema. Cleanup column names refer to the original headers; schema names refer to the renamed output. Transformations run in listed order. decimal_comma changes only plain numbers such as 12,50; ambiguous grouping stays unchanged. No records or columns are removed. Returned csv_text is UTF-8-compatible with CRLF record endings. One paid operation includes cleanup and validation. Spreadsheet-safe escaping is opt-in and may make numeric negative cells fail numeric validation; inspect the final report before import.