Inspect JSON, JSONL, CSV, TSV, YAML, or Parquet shape, types, counts, and a small sample without returning the full payload. Use for 'what fields are in this data?' or unknown tool output. Optionally compare record fields with target_schema and return deterministic mapping candidates; a draft plan is returned only after explicit mappings are supplied. One successful call is sufficient for its recorded observations; never repeat the same arguments to confirm it.

Transform or rewrite records: reshape, filter, join, aggregate, cast, flatten, or convert structured data with Transformation Plan v1. Do not use this tool for a validation-only request such as checking non-null or unique fields; use data_validate. Known transformation shape: {"version":"1","sources":{"input":{"path":"users.json","select":"data.users[*]"}},"steps":[{"id":"filtered","op":"filter","source":"input","where":{"field":"age","gte":18}},{"op":"select","source":"filtered","fields":[{"field":"userId","as":"id"}]}],"return":{"mode":"auto"}}. Omit workspace with ADT_WORKSPACE_ROOT; otherwise it is an exact granted root name, never a path. Returns a compact sample and explicit execution effects; large results require output.path.

Validate or check requirements on existing structured data (校验/检查非空、唯一、类型、字段或行数); choose data_validate, not data_transform, for validation-only requests. It accepts JSON Schema and deterministic assertions and returns valid true or false without rewriting the source. Known validation shape: {"source":{"path":"users.json","select":"data.users[*]"},"assertions":[{"type":"not_null","field":"userId"},{"type":"unique","field":"userId"}]}.

Compare two structured datasets by schema and rows, optionally using stable key fields. Returns compact added, removed, and changed counts and samples.