qlty_distinctCategories

Extract unique values from a specified column in a Teradata database table to identify distinct categories for data analysis and quality assessment.

Instructions

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

Input Schema

TableJSON Schema

Name	Required	Description	Default
`database_name`	Yes
`table_name`	Yes
`column_name`	Yes

Implementation Reference

src/teradata_mcp_server/tools/qlty/qlty_tools.py:72-106 (handler)

Handler function that executes TD_CategoricalSummary to retrieve distinct categories for the specified column in a table, processes results, and returns formatted response with metadata.

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)

src/teradata_mcp_server/tools/module_loader.py:21-34 (registration)

ModuleLoader.MODULE_MAP defines the mapping for 'qlty' module, enabling dynamic loading of qlty tools including handle_qlty_distinctCategories.

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'
}

src/teradata_mcp_server/tools/qlty/__init__.py:1-3 (registration)
Exposes the qlty tools (including handle_qlty_distinctCategories) by importing from qlty_tools.py for use in module loading.
```
from .qlty_resources import *
from .qlty_tools import *
```

Teradata MCP Server