data_product
Extract product values for each column in a dataset. Uses the MCP Data Wrangler server to process and analyze data, enabling structured data transformation and preprocessing tasks.
Instructions
Product values for each column
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input_data_file_path | No | Path to the input data file |
Implementation Reference
- The handler function for the 'data_product' tool. It loads data from the input file path, computes the product for each column, formats it as a dictionary, and returns it as JSON text content.
async def handle_data_product( arguments: dict[str, Any], ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]: data_product_input = DataProductInputSchema.from_args(arguments) product_df = data_product_input.df.product() # Convert the DataFrame to a dictionary format product_dict = { "description": "Product values for each column", "product_values": { col: str(val) if val is not None else None for col, val in zip(product_df.columns, product_df.row(0)) }, } return [ types.TextContent( type="text", text=json.dumps(product_dict), ) ] - Pydantic model and methods defining the input schema and parsing for the data_product tool.
class DataProductInputSchema(Data): model_config = ConfigDict( validate_assignment=True, frozen=True, extra="forbid", arbitrary_types_allowed=True, ) @staticmethod def input_schema() -> dict: return { "type": "object", "properties": { "input_data_file_path": { "type": "string", "description": "Path to the input data file", }, }, } @staticmethod def from_schema(input_data_file_path: str) -> "DataProductInputSchema": data = Data.from_file(input_data_file_path) return DataProductInputSchema(df=data.df) @staticmethod def from_args(arguments: dict[str, Any]) -> "DataProductInputSchema": input_data_file_path = arguments["input_data_file_path"] return DataProductInputSchema.from_schema(input_data_file_path=input_data_file_path) - src/mcp_server_data_wrangler/tools/tools.py:127-131 (registration)Creates and registers the 'data_product' Tool object with MCP types.Tool, specifying name, description, and inputSchema.
types.Tool( name=MCPServerDataWrangler.data_product.value[0], description=MCPServerDataWrangler.data_product.value[1], inputSchema=DataProductInputSchema.input_schema(), ), - src/mcp_server_data_wrangler/tools/tools.py:164-164 (registration)Maps the 'data_product' tool name to its handler function in the tool_to_handler dictionary.
MCPServerDataWrangler.data_product.value[0]: handle_data_product, - src/mcp_server_data_wrangler/tools/tools.py:52-52 (registration)Defines the tool name and description in the MCPServerDataWrangler enum.
data_product = ("data_product", "Product values for each column")