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: List[str]

Implementation Reference

• src/teradata_mcp_server/tools/plot/plot_tools.py:7-36 (handler)

  Handler function executing the core logic of the 'plot_line_chart' tool: validates inputs, queries the Teradata table, and formats data for line chart using helper.

  def handle_plot_line_chart(conn: TeradataConnection, table_name: str, labels: str, columns: str|List[str]):
      """
      Function to generate a line plot for labels and columns.
      Columns mentioned in labels are used for x-axis and columns are used for y-axis.
  
      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: List[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, columns)

• src/teradata_mcp_server/app.py:270-282 (registration)

  Dynamic registration of Python handler functions as MCP tools. Functions named 'handle_<tool_name>' are automatically registered with tool name '<tool_name>' (e.g., 'handle_plot_line_chart' -> 'plot_line_chart'), using signature introspection for schema and docstring for description.

  module_loader = td.initialize_module_loader(config)
  if module_loader:
      all_functions = module_loader.get_all_functions()
      for name, func in all_functions.items():
          if not (inspect.isfunction(func) and name.startswith("handle_")):
              continue
          tool_name = name[len("handle_"):]
          if not any(re.match(p, tool_name) for p in config.get('tool', [])):
              continue
          wrapped = make_tool_wrapper(func)
          mcp.tool(name=tool_name, description=wrapped.__doc__)(wrapped)
          logger.info(f"Created tool: {tool_name}")
  else:

• src/teradata_mcp_server/tools/plot/plot_utils.py:9-72 (helper)

  Core helper utility called by the handler: executes SQL query on Teradata table, processes results into Chart.js-compatible JSON structure for line charts (default chart_type='line'), including predefined colors and metadata.

  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
          })