stats_tool_schemas = [
    {
        "name": "descriptive_stats_func",
        "description": (
            "Computes summary statistics for numeric columns in query.csv: "
            "count, mean, std, min, 25th/50th/75th percentile, and max. "
            "Use when the user asks for summary statistics, descriptive statistics, or a statistical overview. "
            "Returns a formatted HTML table."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "columns": {
                    "type": "array",
                    "description": "Optional list of column names to include. If omitted, all numeric columns from query.csv are used. Avoid ID or index columns.",
                    "items": {"type": "string"},
                },
            },
            "required": [],
        },
    },
    {
        "name": "kmeans_clustering_func",
        "description": (
            "Runs K-Means clustering on numeric feature columns from query.csv. "
            "Groups rows into k clusters, displays a scatter plot coloured by cluster assignment, "
            "and returns a centroid summary table showing the mean of each feature per cluster. "
            "Use when the user asks to cluster the data, find natural segments or groups, or apply K-Means. "
            "Returns an HTML iframe and summary table."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "feature_columns": {
                    "type": "array",
                    "description": "List of numeric column names from query.csv to use as clustering features.",
                    "items": {"type": "string"},
                },
                "x_column": {
                    "type": "string",
                    "description": "Column name from query.csv for the x-axis of the scatter plot. Usually one of the feature columns.",
                },
                "y_column": {
                    "type": "string",
                    "description": "Column name from query.csv for the y-axis of the scatter plot. Usually one of the feature columns.",
                },
                "n_clusters": {
                    "type": "integer",
                    "description": "Number of clusters (k). Default 3. Infer from the user's request.",
                },
                "layout": {
                    "type": "array",
                    "description": "Optional. An array containing a single JSON-formatted Plotly layout dictionary.",
                    "items": {"type": "string"},
                },
            },
            "required": ["feature_columns", "x_column", "y_column"],
        },
    },
    {
        "name": "hypothesis_test_func",
        "description": (
            "Performs a statistical hypothesis test on query.csv data and returns a formatted results table "
            "with test statistic, p-value, and significance at α=0.05. "
            "Supported tests:\n"
            "- 't_test_independent': compare means of a numeric column across two groups "
            "(requires group_column; use group_values if the column has more than 2 unique values).\n"
            "- 't_test_one_sample': test whether a column's mean equals a hypothesized value (requires pop_mean).\n"
            "- 'chi_square': test independence between two categorical columns (requires column and column2)."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "test_type": {
                    "type": "string",
                    "description": "Test to run. One of: 't_test_independent', 't_test_one_sample', 'chi_square'.",
                },
                "column": {
                    "type": "string",
                    "description": "Primary column for the test. Numeric for t-tests; first categorical column for chi-square.",
                },
                "column2": {
                    "type": "string",
                    "description": "Second categorical column. Required for 'chi_square'.",
                },
                "group_column": {
                    "type": "string",
                    "description": "Grouping column. Required for 't_test_independent'. Must have exactly 2 unique values, or specify group_values.",
                },
                "group_values": {
                    "type": "array",
                    "description": "Exactly 2 group labels to compare. Use when group_column has more than 2 unique values.",
                    "items": {"type": "string"},
                },
                "pop_mean": {
                    "type": "number",
                    "description": "Hypothesized population mean (μ₀). Required for 't_test_one_sample'.",
                },
            },
            "required": ["test_type", "column"],
        },
    },
    {
        "name": "regression_func",
        "description": (
            "Runs an OLS linear regression on query.csv data. "
            "Use when the user wants to model the relationship between variables, assess predictors, or run a regression. "
            "Returns a regression summary (coefficients, R², p-values) and a scatter plot with the fitted line as an HTML iframe."
        ),
        "parameters": {
            "type": "object",
            "properties": {
                "independent_variables": {
                    "type": "array",
                    "description": "Column names from query.csv to use as independent (predictor) variables.",
                    "items": {"type": "string"},
                },
                "dependent_variable": {
                    "type": "string",
                    "description": "Column name from query.csv to use as the dependent (outcome) variable.",
                },
                "category": {
                    "type": "string",
                    "description": "Optional column name used to colour-code points and fit separate regression lines per group.",
                },
            },
            "required": ["independent_variables", "dependent_variable"],
        },
    },
]