convert_batch
Convert multiple measurement units simultaneously in a single operation, handling temperature, length, mass, and volume conversions with structured results.
Instructions
Perform multiple unit conversions in a single batch request.
Each request in the batch should contain:
value: The numeric value to convert
from_unit: Source unit for conversion
to_unit: Target unit for conversion
conversion_type: Type of conversion (temperature, length, mass, etc.)
request_id: Optional identifier for tracking individual requests
Returns a structured response with individual results for each conversion, including success/failure status and either converted values or error messages.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| requests | Yes | List of conversion requests. Each request should contain: value (float), from_unit (str), to_unit (str), conversion_type (str), and optionally request_id (str) |
Implementation Reference
- src/unit_converter_mcp/server.py:300-335 (handler)The primary MCP tool handler for 'convert_batch'. This FastMCP-registered function processes the input list of conversion requests, delegates to convert_batch_tool for execution, adds a summary of results, and returns the batch response.
@app.tool() def convert_batch( requests: Annotated[ list[dict], Field( description="List of conversion requests. Each request should contain: value (float), from_unit (str), to_unit (str), conversion_type (str), and optionally request_id (str)" ), ], ) -> dict: """Perform multiple unit conversions in a single batch request. Each request in the batch should contain: - value: The numeric value to convert - from_unit: Source unit for conversion - to_unit: Target unit for conversion - conversion_type: Type of conversion (temperature, length, mass, etc.) - request_id: Optional identifier for tracking individual requests Returns a structured response with individual results for each conversion, including success/failure status and either converted values or error messages. """ results = convert_batch_tool(requests) # Count successful and failed conversions successful = sum(1 for result in results if result["success"]) failed = len(results) - successful return { "batch_results": results, "summary": { "total_requests": len(requests), "successful_conversions": successful, "failed_conversions": failed, }, } - Core implementation of batch conversion logic. Parses requests, validates inputs, routes to appropriate single-unit converters via CONVERSION_FUNCTIONS mapping, handles exceptions, and formats results.
def convert_batch_tool(requests: list[dict[str, Any]]) -> list[dict[str, Any]]: """Perform batch unit conversions. Args: requests: List of conversion request dictionaries, each containing: - value: float - The numeric value to convert - from_unit: str - Source unit - to_unit: str - Target unit - conversion_type: str - Type of conversion - request_id: str (optional) - Identifier for tracking Returns: List of conversion result dictionaries with success/failure status and either converted values or error messages. """ results: list[dict[str, Any]] = [] for request_data in requests: try: # Validate required fields first value = request_data.get("value") from_unit = request_data.get("from_unit") to_unit = request_data.get("to_unit") conversion_type = request_data.get("conversion_type") request_id = request_data.get("request_id") if value is None: raise ValueError("Missing required field: value") if not from_unit: raise ValueError("Missing required field: from_unit") if not to_unit: raise ValueError("Missing required field: to_unit") if not conversion_type: raise ValueError("Missing required field: conversion_type") # Create request object with validated data request = BatchConversionRequest( value=float(value), from_unit=str(from_unit), to_unit=str(to_unit), conversion_type=conversion_type, request_id=request_id, ) # Validate conversion type if request.conversion_type not in CONVERSION_FUNCTIONS: raise ValueError( f"Unsupported conversion type: {request.conversion_type}" ) # Perform the conversion conversion_func = CONVERSION_FUNCTIONS[request.conversion_type] converted_value = conversion_func( request.value, request.from_unit, request.to_unit ) # Create successful result result = BatchConversionResult( request_id=request.request_id, success=True, original_value=request.value, original_unit=request.from_unit, converted_value=converted_value, converted_unit=request.to_unit, conversion_type=request.conversion_type, ) except Exception as e: # Create error result request_id = request_data.get("request_id", f"error_{len(results)}") result = BatchConversionResult( request_id=request_id, success=False, error_message=str(e), ) results.append(result.to_dict()) return results - Type definition (Literal) for valid 'conversion_type' values accepted by the batch tool.
CONVERSION_TYPE = Literal[ "angle", "area", "computer_data", "density", "energy", "force", "length", "mass", "power", "pressure", "speed", "temperature", "time", "volume", ] - Input schema for the 'requests' parameter defining the expected structure of batch conversion requests using Pydantic Field for description.
requests: Annotated[ list[dict], Field( description="List of conversion requests. Each request should contain: value (float), from_unit (str), to_unit (str), conversion_type (str), and optionally request_id (str)" ), ], - Dictionary mapping conversion types to their specific implementation functions, used for dynamic dispatching in convert_batch_tool.
CONVERSION_FUNCTIONS: dict[str, Callable[..., float]] = { "angle": convert_angle_tool, "area": convert_area_tool, "computer_data": convert_computer_data_tool, "density": convert_density_tool, "energy": convert_energy_tool, "force": convert_force_tool, "length": convert_length_tool, "mass": convert_mass_tool, "power": convert_power_tool, "pressure": convert_pressure_tool, "speed": convert_speed_tool, "temperature": convert_temperature_tool, "time": convert_time_tool, "volume": convert_volume_tool, }