Teradata MCP Server

Instructions

Implementation Reference

• src/teradata_mcp_server/tools/plot/plot_tools.py:38-67 (handler)

  Handler function that implements the core logic for the 'plot_polar_chart' tool. It validates that 'labels' is a string and delegates to get_plot_json_data with chart_type='polar'.
  def handle_plot_polar_chart(conn: TeradataConnection, table_name: str, labels: str, column: str): """ Function to generate a polar area 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 column: Required Argument. Specifies the column to be used for generating the line plot. Types: str RETURNS: dict """ # Labels must be always a string which represents a column. if not isinstance(labels, str): raise ValueError("labels must be a string representing the column name for x-axis.") return get_plot_json_data(conn, table_name, labels, column, 'polar')
• src/teradata_mcp_server/tools/plot/plot_utils.py:9-71 (helper)

  Core helper function that executes the SQL query against the Teradata table, fetches data, and formats it as JSON for Chart.js polar charts (sets backgroundColor for 'polar' type). Called by the handler.
  def get_plot_json_data(conn, table_name, labels, columns, chart_type='line'): """ 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 . """ # Define the colors first. colors = ['rgb(75, 192, 192)', '#99cbba', '#d7d0c4', '#fac778', '#e46c59', '#F9CB99', '#280A3E', '#F2EDD1', '#689B8A'] # Chart properties. Every chart needs different property for colors. chart_properties = {'line': 'borderColor', 'polar': 'backgroundColor', 'pie': 'backgroundColor'} 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) # Prepare the statement. with conn.cursor() as cur: recs = cur.execute(sql).fetchall() # Define the structure of the chart data. Below is the structure expected by chart.js # { # labels: labels, # datasets: [{ # label: 'My First Dataset', # data: [65, 59, 80, 81, 56, 55, 40], # fill: false, # borderColor: 'rgb(75, 192, 192)', # tension: 0.1 # }] # } 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, 'borderColor': colors[i], 'fill': False }) # For polar plot, every dataset needs different colors. if chart_type in ('polar', 'pie'): for i, dataset in enumerate(datasets_): # Remove borderColor and add backgroundColor dataset.pop('borderColor', None) dataset['backgroundColor'] = colors 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 {} plot data".format(chart_type), "table_name": table_name, "labels": labels, "columns": columns })