Return a clean, typed copy of a table, with a report of every change and every value left unconverted.

Text: NUL/control/zero-width characters removed, mojibake repaired (Café -> Café), Unicode NFC, spaces collapsed and trimmed. Numbers: plain decimals (1.250,50 € -> 1250.50; (45) -> -45; 1.250,50- -> -1250.50). Dates: ISO 8601. Booleans: true/false. Emails: domain lowercased. Phones: E.164. IBAN: compact uppercase. Values that do not fit the column type are kept as they were (on_invalid='null' empties them) and listed in flags with their source row.

Args: output_format: csv (default) | tsv | json | jsonl | xlsx (typed cells; returned only as content_base64). header_style: keep | snake (Fecha de alta -> fecha_de_alta). columns: keep only these columns, in order. dedupe: none | exact | ignore_case, over all columns or dedupe_keys; the first occurrence is kept. drop_empty_columns: remove columns with no values. output_delimiter: for csv output. formula_guard: prefix text cells starting with = + - @ with ' in CSV so spreadsheets do not run them.

Input (exactly one): content (CSV/TSV/JSON text), content_base64 (file bytes: .xlsx, or CSV in any encoding),
rows (JSON list of objects or of lists). delimiter: auto-detected (, ; tab |). sheet: Excel sheet name (default
the first). has_header: false when the first row is data. header_row: 1-based row of the header (default auto,
skipping title rows above it).
Parsing: number_locale 'eu' (1.234,56) or 'us' (1,234.56) settles values like 1,250 (auto from the column
otherwise). date_order 'dmy' or 'mdy' settles dates like 03/04/2026 (auto from the column otherwise).
phone_country (ISO-2, e.g. ES, US, GB) converts national phone numbers to E.164. null_values: extra strings
read as empty (NULL, N/A, -, #N/A and similar are already). types: force a type per column, e.g.
{"zip": "text", "amount": "decimal"}; types are text, integer, decimal, boolean, date, datetime, email, url,
phone, iban.

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Normalize a list of up to 10,000 values of one kind, e.g. amounts, dates or phone numbers from a document.

kind: auto (inferred like a table column) | number | integer | decimal | date | datetime | boolean | email | url | phone | iban | text. name: optional column name, used as a hint (e.g. 'phone', 'fecha'). Returns one result per input, in order: {"input", "output", "valid"} and "reason" when it could not be converted. Output types: numbers as plain decimal strings, dates ISO 8601, phones E.164.

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Profile a table: what each column really contains and what is wrong with it, without changing it.

Returns the detected encoding/delimiter/header, per-column type, nulls, distinct values, min/max, number locale, date order, currency, examples and notes (leading-zero IDs, formula-injection cells, ambiguous dates or numbers, mixed separators), table issues (ragged rows, NUL bytes, duplicate headers, title rows), exact duplicate rows, the first 50 flagged cells and a suggested schema for validate_schema.

Input (exactly one): content (CSV/TSV/JSON text), content_base64 (file bytes: .xlsx, or CSV in any encoding),
rows (JSON list of objects or of lists). delimiter: auto-detected (, ; tab |). sheet: Excel sheet name (default
the first). has_header: false when the first row is data. header_row: 1-based row of the header (default auto,
skipping title rows above it).
Parsing: number_locale 'eu' (1.234,56) or 'us' (1,234.56) settles values like 1,250 (auto from the column
otherwise). date_order 'dmy' or 'mdy' settles dates like 03/04/2026 (auto from the column otherwise).
phone_country (ISO-2, e.g. ES, US, GB) converts national phone numbers to E.164. null_values: extra strings
read as empty (NULL, N/A, -, #N/A and similar are already). types: force a type per column, e.g.
{"zip": "text", "amount": "decimal"}; types are text, integer, decimal, boolean, date, datetime, email, url,
phone, iban.

Check every row of a table against a schema, after the same parsing clean_table applies.

schema: {"columns": {"id": {"type": "integer", "required": true, "unique": true}, "email": {"type": "email", "required": true}, "amount": {"type": "decimal", "min": 0, "max": 100000}, "signup": {"type": "date", "min": "2020-01-01"}, "status": {"enum": ["active", "churned"]}, "sku": {"type": "text", "pattern": "[A-Z]{3}-\d{4}", "max_length": 8}}, "strict_columns": false} Rules: type, required, unique (case-insensitive), min/max (numbers and dates), min_length/max_length, enum (case-insensitive), pattern (full match; nested quantifiers are refused). Column names match case-insensitively. strict_columns flags columns that are not in the schema. Errors carry the source row number; valid is true only with zero errors. profile_table returns a suggested schema to start from.

Input (exactly one): content (CSV/TSV/JSON text), content_base64 (file bytes: .xlsx, or CSV in any encoding),
rows (JSON list of objects or of lists). delimiter: auto-detected (, ; tab |). sheet: Excel sheet name (default
the first). has_header: false when the first row is data. header_row: 1-based row of the header (default auto,
skipping title rows above it).
Parsing: number_locale 'eu' (1.234,56) or 'us' (1,234.56) settles values like 1,250 (auto from the column
otherwise). date_order 'dmy' or 'mdy' settles dates like 03/04/2026 (auto from the column otherwise).
phone_country (ISO-2, e.g. ES, US, GB) converts national phone numbers to E.164. null_values: extra strings
read as empty (NULL, N/A, -, #N/A and similar are already). types: force a type per column, e.g.
{"zip": "text", "amount": "decimal"}; types are text, integer, decimal, boolean, date, datetime, email, url,
phone, iban.