Teradata MCP Server

Instructions

Implementation Reference

• src/teradata_mcp_server/tools/qlty/qlty_tools.py:12-37 (handler)

  Handler function that executes a Teradata SQL query using TD_ColumnSummary to retrieve columns with missing values (NullCount and NullPercentage), formats results using helper utilities, and returns a standardized response.

  def handle_qlty_missingValues(conn: TeradataConnection, database_name: str | None, table_name: str, *args, **kwargs):
      """
      Get the column names that having missing values in a table.
  
      Arguments:
        database_name - name of the database
        table_name - table name to analyze
  
      Returns:
        ResponseType: formatted response with query results + metadata
      """
      logger.debug(f"Tool: handle_qlty_missingValues: Args: table_name: {database_name}.{table_name}")
  
      if database_name is not None:
              table_name = f"{database_name}.{table_name}"
      with conn.cursor() as cur:
          rows = cur.execute(f"select ColumnName, NullCount, NullPercentage from TD_ColumnSummary ( on {table_name} as InputTable using TargetColumns ('[:]')) as dt ORDER BY NullCount desc")
          data = rows_to_json(cur.description, rows.fetchall())
          metadata = {
              "tool_name": "qlty_missingValues",
              "database_name": database_name,
              "table_name": table_name,
              "rows": len(data)
          }
          logger.debug(f"Tool: handle_qlty_missingValues: Metadata: {metadata}")
          return create_response(data, metadata)

• src/teradata_mcp_server/app.py:461-487 (registration)

  Dynamic registration loop that discovers handle_qlty_missingValues via module loader, derives tool name 'qlty_missingValues' by stripping 'handle_' prefix, wraps the handler for MCP compatibility (injects conn, adapts signature), and registers it as an MCP tool.

  # Register code tools via module loader
  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
          # Skip template tools (used for developer reference only)
          if tool_name.startswith("tmpl_"):
              logger.debug(f"Skipping template tool: {tool_name}")
              continue
          # Skip BAR tools if BAR functionality is disabled
          if tool_name.startswith("bar_") and not enableBAR:
              logger.info(f"Skipping BAR tool: {tool_name} (BAR functionality disabled)")
              continue
          # Skip chat completion tools if chat completion functionality is disabled
          if tool_name.startswith("chat_") and not enableChat:
              logger.info(f"Skipping chat completion tool: {tool_name} (chat completion functionality disabled)")
              continue
          wrapped = make_tool_wrapper(func)
          mcp.tool(name=tool_name, description=wrapped.__doc__)(wrapped)
          logger.info(f"Created tool: {tool_name}")
          logger.debug(f"Tool Docstring: {wrapped.__doc__}")
  else:

• src/teradata_mcp_server/tools/qlty/qlty_tools.py:5-37 (helper)

  Imports utility functions create_response (formats data+metadata into MCP response) and rows_to_json (converts cursor results to JSON). These are used in the handler to process and return results.

  from teradata_mcp_server.tools.utils import create_response, rows_to_json
  
  logger = logging.getLogger("teradata_mcp_server")
  
  #------------------ Tool  ------------------#
  # Missing Values tool
  
  def handle_qlty_missingValues(conn: TeradataConnection, database_name: str | None, table_name: str, *args, **kwargs):
      """
      Get the column names that having missing values in a table.
  
      Arguments:
        database_name - name of the database
        table_name - table name to analyze
  
      Returns:
        ResponseType: formatted response with query results + metadata
      """
      logger.debug(f"Tool: handle_qlty_missingValues: Args: table_name: {database_name}.{table_name}")
  
      if database_name is not None:
              table_name = f"{database_name}.{table_name}"
      with conn.cursor() as cur:
          rows = cur.execute(f"select ColumnName, NullCount, NullPercentage from TD_ColumnSummary ( on {table_name} as InputTable using TargetColumns ('[:]')) as dt ORDER BY NullCount desc")
          data = rows_to_json(cur.description, rows.fetchall())
          metadata = {
              "tool_name": "qlty_missingValues",
              "database_name": database_name,
              "table_name": table_name,
              "rows": len(data)
          }
          logger.debug(f"Tool: handle_qlty_missingValues: Metadata: {metadata}")
          return create_response(data, metadata)