Teradata MCP Server

Instructions

labels:
    Required Argument.
    Specifies the labels to be used for the line plot.
    Types: str

columns:
    Required Argument.
    Specifies the column to be used for generating the line plot.
    Types: str

Implementation Reference

• src/teradata_mcp_server/tools/plot/plot_tools.py:99-127 (handler)

  The handler function implementing the core logic for the 'plot_radar_chart' tool. It validates that 'labels' is a string and calls the helper function to generate radar chart data.

  def handle_plot_radar_chart(conn: TeradataConnection, table_name: str, labels: str, columns: str|List[str]):
      """
      Function to generate a radar plot for labels and columns.
      Columns mentioned in labels are used as labels and column is used to plot.
  
      PARAMETERS:
          table_name:
              Required Argument.
              Specifies the name of the table to generate the donut plot.
              Types: str
  
          labels:
              Required Argument.
              Specifies the labels to be used for the line plot.
              Types: str
  
          columns:
              Required Argument.
              Specifies the column to be used for generating the line plot.
              Types: str
  
      RETURNS:
          dict
      """
      if not isinstance(labels, str):
          raise ValueError("labels must be a string representing the column name for x-axis.")
  
      result = get_radar_plot_json_data(conn, table_name, labels, columns)
      return result

• src/teradata_mcp_server/tools/plot/plot_utils.py:74-151 (helper)

  Helper utility that executes SQL query on the specified table, processes the data into Chart.js compatible radar chart JSON format with predefined colors, and returns a structured response.

  def get_radar_plot_json_data(conn, table_name, labels, columns):
      """
      Helper function to fetch data from a Teradata table and formats it for plotting.
      Right now, designed only to support line plots from chart.js .
      """
      logger.debug(f"Tool: get_json_data_for_plotting")
      # Define the colors first.
      border_colors = [
          'rgb(255, 99, 132)',
          'rgb(54, 162, 235)',
          '#d7d0c4',
          '#fac778',
          '#e46c59',
          '#F9CB99',
          '#280A3E',
          '#F2EDD1',
          '#689B8A'
      ]
      background_colors = [
          'rgba(255, 99, 132, 0.2)',
          'rgba(54, 162, 235, 0.2)',
          'rgb(222, 232, 206, 0.2)',
          'rgb(187, 102, 83, 0.2)',
          'rgb(240, 139, 81, 0.2)',
          'rgb(255, 248, 232, 0.2)'
      ]
      point_background_color = [
          'rgba(255, 99, 132)',
          'rgba(54, 162, 235)',
          'rgb(222, 232, 206)',
          'rgb(187, 102, 83)',
          'rgb(240, 139, 81)',
          'rgb(255, 248, 232)'
      ]
  
      columns = [columns] if isinstance(columns, str) else columns
      sql = "select {labels}, {columns} from {table_name} order by {labels}".format(
            labels=labels, columns=','.join(columns), table_name=table_name)
  
      # Execute the SQL query
  
      # Prepare the statement.
      with conn.cursor() as cur:
          recs = cur.execute(sql).fetchall()
  
      labels = []
      datasets = [[] for _ in range(len(columns))]
      for rec in recs:
          labels.append(rec[0])
          for i_, val in enumerate(rec[1:]):
              datasets[i_].append(val)
  
      # Prepare the datasets for chart.js
      datasets_ = []
      for i, dataset in enumerate(datasets):
          datasets_.append({
              'label': columns[i],
              'data': dataset,
              'fill': True,
              "backgroundColor": background_colors[i % len(background_colors)],
              'borderColor': border_colors[i % len(border_colors)],
              "pointBackgroundColor": point_background_color[i % len(point_background_color)],
              "pointBorderColor": '#fff',
              "pointHoverBackgroundColor": '#fff',
              "pointHoverBorderColor": point_background_color[i % len(point_background_color)]
          })
  
      chart_data = {"labels": [str(l) for l in labels],
                    "datasets": datasets_}
      logger.debug("Chart data: %s", json.dumps(chart_data, indent=2))
  
      return create_response(data=chart_data, metadata={
              "tool_description": "chart js radar plot data",
              "table_name": table_name,
              "labels": labels,
              "columns": columns
          })