Teradata MCP Server

import logging from teradatasql import TeradataConnection 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) #------------------ Tool ------------------# # negative values tool def handle_qlty_negativeValues(conn: TeradataConnection, database_name: str | None, table_name: str, *args, **kwargs): """ Get the column names that having negative 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_negativeValues: 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, NegativeCount from TD_ColumnSummary ( on {table_name} as InputTable using TargetColumns ('[:]')) as dt ORDER BY NegativeCount desc") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_negativeValues", "database_name": database_name, "table_name": table_name, "rows": len(data) } logger.debug(f"Tool: handle_qlty_negativeValues: Metadata: {metadata}") return create_response(data, metadata) #------------------ Tool ------------------# # distinct categories tool def handle_qlty_distinctCategories( conn: TeradataConnection, database_name: str | None, table_name: str, column_name: str, *args, **kwargs ): """ Get the destinct categories from column in 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_distinctCategories: 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_CategoricalSummary ( on {table_name} as InputTable using TargetColumns ('{column_name}')) as dt") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_distinctCategories", "database_name": database_name, "table_name": table_name, "column_name": column_name, "distinct_categories": len(data) } logger.debug(f"Tool: handle_qlty_distinctCategories: Metadata: {metadata}") return create_response(data, metadata) #------------------ Tool ------------------# # standard deviation tool def handle_qlty_standardDeviation( conn: TeradataConnection, database_name: str | None, table_name: str, column_name: str, *args, **kwargs ): """ Get the standard deviation from column in 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_standardDeviation: 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('MEAN','STD')) as dt ORDER BY 1,2") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_standardDeviation", "database_name": database_name, "table_name": table_name, "column_name": column_name, "stats_calculated": ["MEAN", "STD"], "rows": len(data) } logger.debug(f"Tool: handle_qlty_standardDeviation: Metadata: {metadata}") return create_response(data, metadata) #------------------ Tool ------------------# # column summary tool def handle_qlty_columnSummary(conn: TeradataConnection, database_name: str | None, table_name: str, *args, **kwargs): """ Get the column summary statistics for 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_columnSummary: 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 * from TD_ColumnSummary ( on {table_name} as InputTable using TargetColumns ('[:]')) as dt") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_columnSummary", "database_name": database_name, "table_name": table_name, "rows": len(data) } logger.debug(f"Tool: handle_qlty_columnSummary: Metadata: {metadata}") return create_response(data, metadata) #------------------ Tool ------------------# # Univariate statistics tool 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) #------------------ Tool ------------------# # Get Rows with Miissing Values tool def handle_qlty_rowsWithMissingValues( conn: TeradataConnection, database_name: str | None, table_name: str, column_name: str, *args, **kwargs ): """ Get the rows with missing values in 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_rowsWithMissingValues: 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_getRowsWithMissingValues ( ON {table_name} AS InputTable USING TargetColumns ('[{column_name}]')) AS dt;") data = rows_to_json(cur.description, rows.fetchall()) metadata = { "tool_name": "qlty_rowsWithMissingValues", "database_name": database_name, "table_name": table_name, "column_name": column_name, "rows_with_missing_values": len(data) } logger.debug(f"Tool: handle_qlty_rowsWithMissingValues: Metadata: {metadata}") return create_response(data, metadata)