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markomitranic

Data Visualization MCP Server

save_data

Store data in named tables for creating visualizations later. Save datasets to reference when generating charts and graphs with Vega-Lite syntax.

Instructions

A tool which allows you to save data to a named table for later use in visualizations. When to use this tool:

  • Use this tool when you have data that you want to visualize later. How to use this tool:

  • Provide the name of the table to save the data to (for later reference) and the data itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the table to save the data to
dataYesThe data to save

Implementation Reference

  • Handler implementation for the save_data tool: extracts name and data from arguments, stores data in global saved_data dict, returns success message.
    if name == "save_data": save_name = arguments["name"] saved_data[save_name] = arguments["data"] return [types.TextContent(type="text", text=f"Data saved successfully to table {save_name}")]
  • Registration of the save_data tool in list_tools() handler, including name, description, and input schema definition.
    types.Tool( name="save_data", description=SAVE_DATA_TOOL_DESCRIPTION, inputSchema={ "type": "object", "properties": { "name": {"type": "string", "description": "The name of the table to save the data to"}, "data": { "type": "array", "items": {"type": "object", "description": "Row of the table as a dictionary/object"}, "description": "The data to save", }, }, "required": ["name", "data"], }, ),
  • Input schema for save_data tool: requires 'name' (string) and 'data' (array of objects).
    inputSchema={ "type": "object", "properties": { "name": {"type": "string", "description": "The name of the table to save the data to"}, "data": { "type": "array", "items": {"type": "object", "description": "Row of the table as a dictionary/object"}, "description": "The data to save", }, }, "required": ["name", "data"], },
  • Global saved_data dictionary used by save_data handler to store named data tables; pre-populated with sample data.
    saved_data = { "sample_data": [ {"name": "Alice", "age": 25, "city": "New York"}, {"name": "Bob", "age": 30, "city": "San Francisco"}, {"name": "Charlie", "age": 35, "city": "Los Angeles"}, ] }
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