Generates a web-ready interactive Plotly histogram.

Generates a web-ready interactive Plotly scatter plot.

Generates a web-ready interactive Plotly box plot.

Generates a web-ready interactive Plotly line plot.

Generates an interactive Plotly correlation heatmap. Use this to visualize relationships between numeric features. method must be 'pearson' or 'spearman'. column_filter: optional comma-separated column names or suffix patterns (e.g. '_mean') to restrict the heatmap to a subset of columns. Leave empty for all numeric columns.

Generates an interactive Plotly grouped bar chart. x_column: categorical column for the x-axis groups. y_column: numeric column to aggregate. aggregation: how to aggregate y per group — 'mean' (default), 'sum', 'count', or 'median'. color_column: optional column to split bars by colour.

Generates an interactive Plotly scatter matrix (pair plot equivalent). columns: comma-separated list of numeric column names (e.g. 'radius_mean,texture_mean,area_mean'). color_column: optional categorical column to colour points by (e.g. 'diagnosis').

Executes custom Python code (px, pd) to generate complex Plotly charts.

Returns a compact schema of ALL columns in one call: column names grouped by type (numeric, categorical, datetime). Categorical columns also show their unique values. Call this FIRST to understand the dataset structure, then call plot or stats tools.

Analyzes a specific column in the dataset and returns a statistical summary. Use this for a deep dive into one column after using get_all_columns_summary.

Generates a static Matplotlib/Seaborn histogram (for papers/publications).

Generates a static Matplotlib/Seaborn scatter plot (for papers/publications).

Generates a static Matplotlib/Seaborn box plot (for papers/publications).

Generates a static Matplotlib/Seaborn line plot (for papers/publications).

Generates a static Seaborn bar chart (for papers/publications). x_column: categorical column for the x-axis groups. y_column: numeric column to aggregate. aggregation: how to aggregate y per group — 'mean' (default), 'sum', 'count', or 'median'. hue_column: optional column to split bars by colour.

Executes custom Python code (plt, sns, pd) to generate complex static charts.

Generates a Seaborn pair plot (scatter matrix) for the specified columns. Use this for multi-feature distribution and correlation exploration. columns: comma-separated list of numeric column names (e.g. 'radius_mean,texture_mean,area_mean'). hue_column: optional categorical column name to colour points by (e.g. 'diagnosis'). Leave empty if not needed.

Generates a static Word Cloud image from a column containing text data. Use this when the user wants to visualize the most frequent terms in a dataset. extra_stopwords: optional comma-separated words to exclude (e.g. "said,also,one").

Generates a publication-ready Seaborn correlation heatmap. Use this when the user explicitly asks for static or publication figures. method must be 'pearson' or 'spearman'. column_filter: optional comma-separated column names or suffix patterns (e.g. '_mean') to restrict the heatmap to a subset of columns. Leave empty for all numeric columns.

Computes statistical correlation (pearson, spearman) between two numeric columns. Use this to mathematically verify relationships before plotting scatterplots.

Performs T-tests (2 groups) or ANOVA (>2 groups) to see if a numeric variable (target_col) differs significantly across categories (group_col). Use this before generating boxplots.

Runs an OLS Linear Regression. target_col is the dependent variable (Y). predictor_cols is a list of independent variables (X). CRITICAL: predictor_cols MUST be a valid JSON array of strings, e.g., ["col1", "col2"].

Calculates and ranks the correlation between a single target column and all other numeric columns in the dataset at once. Use this tool when the user wants to rank, sort, or find top features related to a specific outcome column like diagnosis.

Fetch a webpage and return structured content: title, meta description, navigation items, page headings, CSS color palette, font families, and main page text (up to 4000 chars). Use this to research a site before cloning its design, extract information, or understand its structure. Pair with screenshot_webpage to also see how it looks visually.

Search the internet using DuckDuckGo and return titles, URLs, and text snippets. No API key required. Use this to find documentation, discover libraries, look up best practices, or research any topic before starting a coding task. max_results: number of results to return (1-10, default 5).

Take a 1440x900 screenshot of a webpage using headless Chromium and save it as a PNG. Returns the file path of the saved screenshot. Useful for visually inspecting a site's appearance and layout before cloning its design. save_path: optional absolute path for the PNG; auto-generated if omitted. Requires playwright: pip install playwright && playwright install chromium