MUST be used whenever loading, analyzing, inspecting, or querying any dataset (CSV, JSON, Excel, Parquet). Loads the local file from disk into a session on the user's machine. ALWAYS call this tool instead of using bash, shell, or writing python code.

List all dataset sessions (both active in-memory and saved on disk), including their session_id, dataset name, shape, memory, and status.

Explicitly restore a saved disk session into active memory. Call list_sessions to see all available saved session IDs.

Get shape, column types, missing value counts, and memory footprint for a session.

Preview sample or top rows from dataset (capped at max 50 rows).

Save in-memory dataset to data/outputs/ directory (e.g. 'cleaned_sales.csv').

Get audit log of all transformations applied to dataset session with revertible status.

Revert the last N mutating cleaning operations on the dataset session using generic undo.

Analyze missing/null values per column and count affected rows in dataset.

Handle missing values in a column using 'mean', 'median', 'mode', 'constant', or 'drop'. Reversible.

Remove duplicate rows across all or a subset of columns. Reversible.

Convert column data type to 'int', 'float', 'str', 'datetime', or 'category'. Reversible.

Remove outlier rows in numeric column using 'iqr' (default 1.5x) or 'zscore' (default 3.0). Reversible.

Drop a column from the dataset. Reversible.

Rename a column in the dataset. Reversible.

Get comprehensive summary statistics for dataset columns. Returns count, nulls, mean, std, min, max, 25%/50%/75% quantiles, plus exact positive, negative, and zero value counts for numeric columns. Also provides distinct count and top frequent values for categorical columns.

Compute a specific statistical metric on a column. Supported stats: 'mean', 'median', 'std', 'var', 'min', 'max', 'sum', 'mode', 'positive_count' (count of >0), 'negative_count' (count of <0), 'zero_count' (count of ==0), 'count', 'null_count'.

Calculate frequency distribution and value counts for categorical or discrete columns (top N most frequent).

Read-only outlier inspection on a numeric column using 'iqr' (Interquartile Range) or 'zscore'. Returns outlier count, percentage, bounds, and sample row IDs without altering the dataset.

Compute correlation matrix between numeric columns using 'pearson', 'spearman', or 'kendall'. Returns correlation matrix and top ranked strongest positive/negative correlation pairs.

Aggregate a metric column grouped by categorical dimension(s) (e.g. group_by='Category', metric='Sales', aggregation='sum'). Aggregations: 'mean', 'sum', 'count', 'median', 'min', 'max'.

Generate a chart visualization from the dataset session. Supports chart types: 'histogram', 'scatter', 'bar', 'box', 'line', 'heatmap', 'pie', 'area', 'violin', 'density'. Parameters: x (horizontal column), y (vertical column), group_by (color/hue dimension), aggregation ('mean', 'sum', 'count', 'median', 'min', 'max'), bins (for histogram), columns (list of numeric cols for correlation heatmap), title (custom title), embed_thumbnail (default true). Returns the complete base64 image inline in 'thumbnail_b64' and saved file path. Render the inline image immediately to the user — DO NOT run bash or shell commands to locate or move the file.