qlty_univariateStatistics
Analyze column data distributions in Teradata tables by calculating univariate statistics to assess data quality and understand patterns.
Instructions
Get the univariate statistics for a table.
Arguments: database_name - name of the database table_name - table name to analyze column_name - column name to analyze
Returns: ResponseType: formatted response with query results + metadata
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| database_name | Yes | ||
| table_name | Yes | ||
| column_name | Yes |
Implementation Reference
- The core handler function that executes the tool logic: queries TD_UnivariateStatistics for all stats on the given table.column, formats results as JSON with metadata.def handle_qlty_univariateStatistics( conn: TeradataConnection, database_name: str | None, table_name: str, column_name: str, *args, **kwargs ): """ Get the univariate statistics for a table. Arguments: database_name - name of the database table_name - table name to analyze column_name - column name to analyze Returns: ResponseType: formatted response with query results + metadata """ logger.debug(f"Tool: handle_qlty_univariateStatistics: Args: table_name: {database_name}.{table_name}, column_name: {column_name}") if database_name is not None: table_name = f"{database_name}.{table_name}" with conn.cursor() as cur: rows = cur.execute(f"select * from TD_UnivariateStatistics ( on {table_name} as InputTable using TargetColumns ('{column_name}') Stats('ALL')) as dt ORDER BY 1,2") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_univariateStatistics", "database_name": database_name, "table_name": table_name, "column_name": column_name, "stats_calculated": ["ALL"], "rows": len(data) } logger.debug(f"Tool: handle_qlty_univariateStatistics: Metadata: {metadata}") return create_response(data, metadata)
- src/teradata_mcp_server/app.py:462-487 (registration)Dynamic registration of the tool: loads modules via ModuleLoader, finds handle_qlty_univariateStatistics function, derives tool name 'qlty_univariateStatistics', wraps it for MCP (injects conn, handles QueryBand), and registers with mcp.tool().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:
- ModuleLoader MODULE_MAP defines the 'qlty' module path for dynamic loading of qlty_tools.py containing the handler.MODULE_MAP = { 'bar': 'teradata_mcp_server.tools.bar', 'base': 'teradata_mcp_server.tools.base', 'chat': 'teradata_mcp_server.tools.chat', 'dba': 'teradata_mcp_server.tools.dba', 'fs': 'teradata_mcp_server.tools.fs', 'qlty': 'teradata_mcp_server.tools.qlty', 'rag': 'teradata_mcp_server.tools.rag', 'sql_opt': 'teradata_mcp_server.tools.sql_opt', 'sec': 'teradata_mcp_server.tools.sec', 'tmpl': 'teradata_mcp_server.tools.tmpl', 'plot': 'teradata_mcp_server.tools.plot', 'tdvs': 'teradata_mcp_server.tools.tdvs' }