AImart Parquet

Convert a messy data file into clean, typed, zstd-compressed Parquet. Use this first for any CSV/TSV, JSON/NDJSON, Excel, SPSS, Stata or SAS file, or a digital PDF with tables. It detects the format, encoding and delimiter; skips title rows; repairs broken JSON; pulls tables out of PDFs (merging tables that continue across pages); infers int/float/bool/date/timestamp columns (including 1.234,56 and $1,234 formats) while keeping IDs with leading zeros as text; normalizes column names to snake_case; keeps SPSS/Stata/SAS variable and value labels; and can validate against an expected_schema. Returns an artifact_id (pass it to inspect_parquet / preview_parquet / query_parquet), a download link, and a per-column report: type, null count, values that did not fit the chosen type, and warnings. Price: $0.02 USDC per successful call, paid via x402.

Describe a Parquet file from its footer without reading rows: row count, columns, types, null counts, min/max per column, and SPSS/Stata/SAS labels. The cheapest way to understand a dataset before querying it. Input: artifact_id from convert_to_parquet, or a url to a .parquet file. Price: $0.002 USDC per successful call, paid via x402.

Return a slice of rows from a Parquet file (default 20, max 500), optionally only some columns, as JSON rows, a Markdown table, or CSV. Use to eyeball data; use query_parquet to filter or aggregate. Long cells are truncated. Price: $0.002 USDC per successful call, paid via x402.

Run one read-only DuckDB SELECT over a Parquet file, available as table `data`; join more files by passing `tables` ({name: artifact_id}, up to 7 besides data), e.g. a GL against a bank statement. Supports aggregates, window functions, CTEs, DESCRIBE data and SUMMARIZE data. Up to 1000 rows are returned; `truncated` says if more matched. Writes, file access and multiple statements are rejected. Example: SELECT region, sum(units) FROM data GROUP BY 1 ORDER BY 2 DESC. Price: $0.005 USDC per successful call, paid via x402.

Free. If none of these tools does what you need — a file type we reject, an output we do not produce, a missing operation — describe the task, the formats involved, and the most you would pay per call. The operator reads these to decide what to build next. Do not include personal or confidential data.

Free. Check that a file this service produced is authentic and unaltered: verifies the post-quantum ML-DSA-65 (NIST FIPS 204) signature in an artifact's `provenance`, and with artifact_id also re-checks the stored file's hash and size against the signed manifest.