array_operations
Perform element-wise mathematical operations on 2D arrays using Polars. Supports array-array and array-scalar calculations including addition, subtraction, multiplication, division, and power operations.
Instructions
Perform element-wise operations on arrays using Polars.
Supports array-array and array-scalar operations.
Examples:
SCALAR MULTIPLICATION: operation="multiply", array1=[[1,2],[3,4]], array2=2 Result: [[2,4],[6,8]]
ARRAY ADDITION: operation="add", array1=[[1,2]], array2=[[3,4]] Result: [[4,6]]
POWER OPERATION: operation="power", array1=[[2,3]], array2=2 Result: [[4,9]]
ARRAY DIVISION: operation="divide", array1=[[10,20],[30,40]], array2=[[2,4],[5,8]] Result: [[5,5],[6,5]]
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 |
| operation | Yes | Element-wise operation to perform | |
| array1 | Yes | First 2D array (e.g., [[1,2],[3,4]]) | |
| array2 | Yes | Second array, scalar, or JSON string |
Implementation Reference
- src/vibe_math_mcp/tools/array.py:42-95 (handler)The main handler function for the 'array_operations' tool. Performs element-wise arithmetic operations on 2D arrays (or array-scalar) using Polars DataFrames for add/subtract/multiply/divide and NumPy for power. Handles JSON string inputs from MCP serialization, division by zero, and formats output with shape info.async def array_operations( operation: Annotated[ Literal["add", "subtract", "multiply", "divide", "power"], Field(description="Element-wise operation to perform"), ], array1: Annotated[List[List[float]], Field(description="First 2D array (e.g., [[1,2],[3,4]])")], array2: Annotated[ Union[str, List[List[float]], float], Field(description="Second array, scalar, or JSON string"), ], ) -> str: """Element-wise array operations.""" try: # Handle XML serialization: parse stringified JSON if isinstance(array2, str): try: array2 = cast(List[List[float]], json.loads(array2)) except (json.JSONDecodeError, ValueError): array2 = cast(float, float(array2)) df1 = list_to_polars(array1) is_scalar = isinstance(array2, (int, float)) if operation == "add": result_df = df1 + array2 if is_scalar else df1 + list_to_polars(array2) elif operation == "subtract": result_df = df1 - array2 if is_scalar else df1 - list_to_polars(array2) elif operation == "multiply": result_df = df1 * array2 if is_scalar else df1 * list_to_polars(array2) elif operation == "divide": if is_scalar and array2 == 0: raise ValueError("Division by zero") result_df = df1 / array2 if is_scalar else df1 / list_to_polars(array2) elif operation == "power": # Use NumPy for reliable power operations (Polars doesn't support ** operator) arr1 = df1.to_numpy() if is_scalar: result_arr = arr1**array2 else: arr2 = list_to_polars(array2).to_numpy() result_arr = arr1**arr2 result_df = list_to_polars(result_arr.tolist()) else: raise ValueError(f"Unknown operation: {operation}") result = polars_to_list(result_df) return format_array_result( result, {"operation": operation, "shape": f"{len(result)}×{len(result[0])}"} ) except Exception as e: raise ValueError(f"Array operation failed: {str(e)}")
- src/vibe_math_mcp/tools/array.py:13-41 (registration)Registers the 'array_operations' tool with the MCP server using the @mcp.tool decorator. Includes detailed description with examples and tool annotations indicating it's read-only and idempotent.@mcp.tool( name="array_operations", description="""Perform element-wise operations on arrays using Polars. Supports array-array and array-scalar operations. Examples: SCALAR MULTIPLICATION: operation="multiply", array1=[[1,2],[3,4]], array2=2 Result: [[2,4],[6,8]] ARRAY ADDITION: operation="add", array1=[[1,2]], array2=[[3,4]] Result: [[4,6]] POWER OPERATION: operation="power", array1=[[2,3]], array2=2 Result: [[4,9]] ARRAY DIVISION: operation="divide", array1=[[10,20],[30,40]], array2=[[2,4],[5,8]] Result: [[5,5],[6,5]]""", annotations=ToolAnnotations( title="Array Operations", readOnlyHint=True, idempotentHint=True, ), )
- Pydantic-based input schema definition using Annotated types and Field descriptions for operation type, array1 (2D list of floats), and array2 (flexible: str/2D list/scalar).operation: Annotated[ Literal["add", "subtract", "multiply", "divide", "power"], Field(description="Element-wise operation to perform"), ], array1: Annotated[List[List[float]], Field(description="First 2D array (e.g., [[1,2],[3,4]])")], array2: Annotated[ Union[str, List[List[float]], float], Field(description="Second array, scalar, or JSON string"), ], ) -> str:
- src/vibe_math_mcp/server.py:693-706 (helper)Imports the tools modules in server.py, which triggers the execution of decorators and registers all tools including array_operations with the MCP instance.from .tools import array, basic, batch, calculus, financial, linalg, statistics # noqa: E402 # Explicitly declare as part of module interface (tools registered via decorators) __all__ = ["mcp", "basic", "array", "batch", "statistics", "financial", "linalg", "calculus"] def main(): """Entry point for uvx.""" mcp.run() if __name__ == "__main__": main()