array_statistics
Calculate statistical measures like mean, median, and standard deviation on 2D arrays. Compute across entire arrays, rows, or columns to analyze data patterns.
Instructions
Calculate statistical measures on arrays using Polars.
Supports computation across entire array, rows, or columns.
Examples:
COLUMN-WISE MEANS: data=[[1,2,3],[4,5,6]], operations=["mean"], axis=0 Result: [2.5, 3.5, 4.5] (average of each column)
ROW-WISE MEANS: data=[[1,2,3],[4,5,6]], operations=["mean"], axis=1 Result: [2.0, 5.0] (average of each row)
OVERALL STATISTICS: data=[[1,2,3],[4,5,6]], operations=["mean","std"], axis=None Result: {mean: 3.5, std: 1.71}
MULTIPLE STATISTICS: data=[[1,2,3],[4,5,6]], operations=["min","max","mean"], axis=0 Result: {min: [1,2,3], max: [4,5,6], mean: [2.5,3.5,4.5]}
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional annotation to label this calculation (e.g., 'Bond A PV', 'Q2 revenue'). Appears in results for easy identification. | |
| output_mode | No | Output format: full (default), compact, minimal, value, or final. See batch_execute tool for details. | full |
| data | Yes | 2D array (e.g., [[1,2,3],[4,5,6]]) | |
| operations | Yes | Statistics to compute (e.g., ['mean','std']) | |
| axis | No | Axis: 0=column-wise, 1=row-wise, None=overall |
Implementation Reference
- src/vibe_math_mcp/tools/array.py:97-191 (handler)Full implementation of the array_statistics tool handler, including decorator for registration, input schema via Annotated types, and execution logic using Polars DataFrames and NumPy for various statistics (mean, median, std, min, max, sum) along different axes.@mcp.tool( name="array_statistics", description="""Calculate statistical measures on arrays using Polars. Supports computation across entire array, rows, or columns. Examples: COLUMN-WISE MEANS: data=[[1,2,3],[4,5,6]], operations=["mean"], axis=0 Result: [2.5, 3.5, 4.5] (average of each column) ROW-WISE MEANS: data=[[1,2,3],[4,5,6]], operations=["mean"], axis=1 Result: [2.0, 5.0] (average of each row) OVERALL STATISTICS: data=[[1,2,3],[4,5,6]], operations=["mean","std"], axis=None Result: {mean: 3.5, std: 1.71} MULTIPLE STATISTICS: data=[[1,2,3],[4,5,6]], operations=["min","max","mean"], axis=0 Result: {min: [1,2,3], max: [4,5,6], mean: [2.5,3.5,4.5]}""", annotations=ToolAnnotations( title="Array Statistics", readOnlyHint=True, idempotentHint=True, ), ) async def array_statistics( data: Annotated[List[List[float]], Field(description="2D array (e.g., [[1,2,3],[4,5,6]])")], operations: Annotated[ List[Literal["mean", "median", "std", "min", "max", "sum"]], Field(description="Statistics to compute (e.g., ['mean','std'])"), ], axis: Annotated[ int | None, Field(description="Axis: 0=column-wise, 1=row-wise, None=overall") ] = None, ) -> str: """Calculate array statistics.""" try: df = list_to_polars(data) results = {} for op in operations: if axis is None: # Overall statistics across all values all_values = df.to_numpy().flatten() if op == "mean": results[op] = float(np.mean(all_values)) elif op == "median": results[op] = float(np.median(all_values)) elif op == "std": results[op] = float(np.std(all_values, ddof=1)) elif op == "min": results[op] = float(np.min(all_values)) elif op == "max": results[op] = float(np.max(all_values)) elif op == "sum": results[op] = float(np.sum(all_values)) elif axis == 0: # Column-wise statistics if op == "mean": results[op] = df.mean().to_numpy()[0].tolist() elif op == "median": results[op] = df.median().to_numpy()[0].tolist() elif op == "std": results[op] = df.std().to_numpy()[0].tolist() elif op == "min": results[op] = df.min().to_numpy()[0].tolist() elif op == "max": results[op] = df.max().to_numpy()[0].tolist() elif op == "sum": results[op] = df.sum().to_numpy()[0].tolist() elif axis == 1: # Row-wise statistics arr = df.to_numpy() if op == "mean": results[op] = np.mean(arr, axis=1).tolist() elif op == "median": results[op] = np.median(arr, axis=1).tolist() elif op == "std": results[op] = np.std(arr, axis=1, ddof=1).tolist() elif op == "min": results[op] = np.min(arr, axis=1).tolist() elif op == "max": results[op] = np.max(arr, axis=1).tolist() elif op == "sum": results[op] = np.sum(arr, axis=1).tolist() return format_result(results, {"shape": f"{len(data)}×{len(data[0])}", "axis": axis}) except Exception as e: raise ValueError(f"Statistics calculation failed: {str(e)}")
- src/vibe_math_mcp/server.py:693-693 (registration)Import statement that loads the tools module containing array_statistics, triggering its registration via the @mcp.tool decorator on the CustomMCP server instance.from .tools import array, basic, batch, calculus, financial, linalg, statistics # noqa: E402