List every NetSuite saved-search export (.xls) in a directory under NSMCP_ROOT. Returns one ExportSummary per file with row count, header count, 0-indexed header_row, warning count, and detected date range. Files that can't be parsed even with lxml recovery come back with parse_error populated and the other fields None. Call this first when you don't already know which exports are available.

Return the column headers of a NetSuite saved-search export plus their spreadsheet column letters (A, B, ..., AA, AB) and the 0-indexed header_row. Call this before query_export or aggregate_export when you don't already know the column names — every other tool takes column names verbatim and errors on typos with a difflib suggestion.

Filter rows from a NetSuite export by a list of predicates (AND-combined; empty list returns everything). Predicate ops: eq/ne, gt/gte/lt/lte, contains/not_contains (case-insensitive by default), regex, date_range (ISO 8601 start/end, inclusive by default). Optionally project to a subset of columns via the columns argument. Implicit limit=1000; pass limit=0 to get total_matched without fetching any rows. Returns rows, total_matched, and a truncated flag.

Group rows from a NetSuite export by one or more columns and compute aggregations per group. Each Measure carries column, op (sum/count/avg/min/max), and optional alias for the output key (defaults to {op}_{column}). Groups are returned in first-seen order. Use this instead of query_export when you want summary statistics rather than raw rows.

Tag every row with a derived _category based on case-insensitive substring matches across one or more memo columns. NetSuite GL exports usually carry both 'Memo (main)' and 'Memo (line)'; pass both so the keyword sweep covers all the prose. rules maps category name to a list of keywords; the first rule whose keyword appears in any memo wins; rows matching nothing fall into 'Uncategorized'. Returns the tagged rows plus a per-category count breakdown.

Run three anomaly checks against a NetSuite GL-style export and return Findings: (1) zero_activity_period — month gaps inside the observed period range (HIGH); (2) ratio_anomaly — (account, period) total greater than 2x the account's median total across periods (MEDIUM); (3) document_count_variance — period row count more than 2 stdev from the mean across periods (MEDIUM). Each Finding includes severity, description, up to 10 supporting_rows, and total_supporting_count for the true un-truncated count. The period column should contain labels like 'Jan 2024', 'January 2024', or '2024-01'.

Return the parse warnings for the export at file_path, parsing it on demand if it isn't already cached. Warning kinds: phantom_column (cell at a column index beyond the header count), bad_datetime (DateTime cell that wouldn't parse — raw string is preserved in the row), encoding_recovery (lxml had to recover from invalid XML), empty_row_skipped. Call this after any other tool reports a non-zero warning_count to see exactly which rows are affected.