array_transform
Transform arrays for machine learning preprocessing and data normalization using L2 normalization, standardization, min-max scaling, or log transformation.
Instructions
Transform arrays for ML preprocessing and data normalization.
Transformations: - normalize: L2 normalization (unit vector) - standardize: Z-score (mean=0, std=1) - minmax_scale: Scale to [0,1] range - log_transform: Natural log transform
Examples:
L2 NORMALIZATION: data=[[3,4]], transform="normalize" Result: [[0.6,0.8]] (3²+4²=25, √25=5, 3/5=0.6, 4/5=0.8)
STANDARDIZATION (Z-SCORE): data=[[1,2],[3,4]], transform="standardize" Result: Values with mean=0, std=1
MIN-MAX SCALING: data=[[1,2],[3,4]], transform="minmax_scale" Result: [[0,0.33],[0.67,1]] (scaled to [0,1])
LOG TRANSFORM: data=[[1,10,100]], transform="log_transform" Result: [[0,2.3,4.6]] (natural log)
Input 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 to transform (e.g., [[1,2],[3,4]]) | |
| transform | Yes | Transformation type | |
| axis | No | Axis: 0=column-wise, 1=row-wise, None=overall |
Input Schema (JSON Schema)
{
"properties": {
"axis": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"description": "Axis: 0=column-wise, 1=row-wise, None=overall"
},
"context": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional annotation to label this calculation (e.g., 'Bond A PV', 'Q2 revenue'). Appears in results for easy identification."
},
"data": {
"description": "2D array to transform (e.g., [[1,2],[3,4]])",
"items": {
"items": {
"type": "number"
},
"type": "array"
},
"type": "array"
},
"output_mode": {
"default": "full",
"description": "Output format: full (default), compact, minimal, value, or final. See batch_execute tool for details.",
"enum": [
"full",
"compact",
"minimal",
"value",
"final"
],
"type": "string"
},
"transform": {
"description": "Transformation type",
"enum": [
"normalize",
"standardize",
"minmax_scale",
"log_transform"
],
"type": "string"
}
},
"required": [
"transform",
"data"
],
"type": "object"
}
Implementation Reference
- src/vibe_math_mcp/tools/array.py:272-375 (handler)Full implementation of the array_transform tool, including decorator registration, input schema (data: List[List[float]], transform: Literal[...], axis: Optional[int]), and handler logic for normalize/standardize/minmax_scale/log_transform using NumPy with axis support.name="array_transform", description="""Transform arrays for ML preprocessing and data normalization. Transformations: - normalize: L2 normalization (unit vector) - standardize: Z-score (mean=0, std=1) - minmax_scale: Scale to [0,1] range - log_transform: Natural log transform Examples: L2 NORMALIZATION: data=[[3,4]], transform="normalize" Result: [[0.6,0.8]] (3²+4²=25, √25=5, 3/5=0.6, 4/5=0.8) STANDARDIZATION (Z-SCORE): data=[[1,2],[3,4]], transform="standardize" Result: Values with mean=0, std=1 MIN-MAX SCALING: data=[[1,2],[3,4]], transform="minmax_scale" Result: [[0,0.33],[0.67,1]] (scaled to [0,1]) LOG TRANSFORM: data=[[1,10,100]], transform="log_transform" Result: [[0,2.3,4.6]] (natural log)""", annotations=ToolAnnotations( title="Array Transformation", readOnlyHint=True, idempotentHint=True, ), ) async def array_transform( data: Annotated[ List[List[float]], Field(description="2D array to transform (e.g., [[1,2],[3,4]])") ], transform: Annotated[ Literal["normalize", "standardize", "minmax_scale", "log_transform"], Field(description="Transformation type"), ], axis: Annotated[ int | None, Field(description="Axis: 0=column-wise, 1=row-wise, None=overall") ] = None, ) -> str: """Transform arrays.""" try: arr = list_to_numpy(data) if transform == "normalize": # L2 normalization if axis is None: norm = np.linalg.norm(arr) result = (arr / norm if norm != 0 else arr).tolist() elif axis == 0: norms = np.linalg.norm(arr, axis=0, keepdims=True) result = (arr / np.where(norms != 0, norms, 1)).tolist() else: norms = np.linalg.norm(arr, axis=1, keepdims=True) result = (arr / np.where(norms != 0, norms, 1)).tolist() elif transform == "standardize": # Z-score standardization if axis is None: mean = np.mean(arr) std = np.std(arr, ddof=1) result = ((arr - mean) / std if std != 0 else arr - mean).tolist() elif axis == 0: mean = np.mean(arr, axis=0, keepdims=True) std = np.std(arr, axis=0, ddof=1, keepdims=True) result = ((arr - mean) / np.where(std != 0, std, 1)).tolist() else: mean = np.mean(arr, axis=1, keepdims=True) std = np.std(arr, axis=1, ddof=1, keepdims=True) result = ((arr - mean) / np.where(std != 0, std, 1)).tolist() elif transform == "minmax_scale": # Min-Max scaling to [0, 1] if axis is None: min_val = np.min(arr) max_val = np.max(arr) range_val = max_val - min_val result = ((arr - min_val) / range_val if range_val != 0 else arr - min_val).tolist() elif axis == 0: min_val = np.min(arr, axis=0, keepdims=True) max_val = np.max(arr, axis=0, keepdims=True) range_val = max_val - min_val result = ((arr - min_val) / np.where(range_val != 0, range_val, 1)).tolist() else: min_val = np.min(arr, axis=1, keepdims=True) max_val = np.max(arr, axis=1, keepdims=True) range_val = max_val - min_val result = ((arr - min_val) / np.where(range_val != 0, range_val, 1)).tolist() elif transform == "log_transform": # Natural log transform (handles negatives by using log1p) result = np.log1p(np.abs(arr) * np.sign(arr)).tolist() else: raise ValueError(f"Unknown transform: {transform}") return format_array_result(result, {"transform": transform, "axis": axis}) except Exception as e: raise ValueError(f"Transformation failed: {str(e)}")
- src/vibe_math_mcp/tools/array.py:272-303 (registration)Tool registration decorator @mcp.tool defining name, description, and annotations for array_transform.name="array_transform", description="""Transform arrays for ML preprocessing and data normalization. Transformations: - normalize: L2 normalization (unit vector) - standardize: Z-score (mean=0, std=1) - minmax_scale: Scale to [0,1] range - log_transform: Natural log transform Examples: L2 NORMALIZATION: data=[[3,4]], transform="normalize" Result: [[0.6,0.8]] (3²+4²=25, √25=5, 3/5=0.6, 4/5=0.8) STANDARDIZATION (Z-SCORE): data=[[1,2],[3,4]], transform="standardize" Result: Values with mean=0, std=1 MIN-MAX SCALING: data=[[1,2],[3,4]], transform="minmax_scale" Result: [[0,0.33],[0.67,1]] (scaled to [0,1]) LOG TRANSFORM: data=[[1,10,100]], transform="log_transform" Result: [[0,2.3,4.6]] (natural log)""", annotations=ToolAnnotations( title="Array Transformation", readOnlyHint=True, idempotentHint=True, ), )
- Input schema defined via Annotated types and Field descriptions for data, transform, and axis parameters.data: Annotated[ List[List[float]], Field(description="2D array to transform (e.g., [[1,2],[3,4]])") ], transform: Annotated[ Literal["normalize", "standardize", "minmax_scale", "log_transform"], Field(description="Transformation type"), ], axis: Annotated[ int | None, Field(description="Axis: 0=column-wise, 1=row-wise, None=overall") ] = None, ) -> str: