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Teradata MCP Server

dba_tableUsageImpact

Analyze table and view usage by users to identify resource consumption patterns and optimize database performance in Teradata environments.

Instructions

Measure the usage of a table and views by users, this is helpful to understand what user and tables are driving most resource usage at any point in time.

Arguments: database_name - database name to analyze user_name - user name to analyze

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
database_nameNo
user_nameNo

Implementation Reference

  • Handler function for the dba_tableUsageImpact tool. Executes SQL query on DBQL tables to compute table usage statistics including query counts, first/last query timestamps, and usage frequency categories (High/Medium/Low). Supports optional filtering by database_name or user_name.
    def handle_dba_tableUsageImpact(conn: TeradataConnection, database_name: str | None = None, user_name: str | None = None, *args, **kwargs):
        """
        Measure the usage of a table and views by users, this is helpful to understand what user and tables are driving most resource usage at any point in time.
    
        Arguments:
          database_name - database name to analyze
          user_name - user name to analyze
    
        """
        logger.debug(f"Tool: handle_dba_tableUsageImpact: Args: database_name: {database_name}, user_name: {user_name}")
        database_name_filter = f"AND objectdatabasename = '{database_name}'" if database_name else ""
        user_name_filter = f"AND username = '{user_name}'" if user_name else ""
        table_usage_sql="""
        LOCKING ROW for ACCESS
        sel
        DatabaseName
        ,TableName
        ,UserName
        ,Weight as "QueryCount"
        ,100*"Weight" / sum("Weight") over(partition by 1) PercentTotal
        ,case
            when PercentTotal >=10 then 'High'
            when PercentTotal >=5 then 'Medium'
            else 'Low'
        end (char(6)) usage_freq
        ,FirstQueryDaysAgo
        ,LastQueryDaysAgo
    
        from
        (
            SELECT   TRIM(QTU1.TableName)  AS "TableName"
                    , TRIM(QTU1.DatabaseName)  AS "DatabaseName"
                    ,UserName as "UserName"
                    ,max((current_timestamp - CollectTimeStamp) day(4)) as "FirstQueryDaysAgo"
                    ,min((current_timestamp - CollectTimeStamp) day(4)) as "LastQueryDaysAgo"
                    , COUNT(DISTINCT QTU1.QueryID) as "Weight"
            FROM    (
                        SELECT   objectdatabasename AS DatabaseName
                            , ObjectTableName AS TableName
                            , ob.QueryId
                        FROM DBC.DBQLObjTbl ob /* uncomment for DBC */
                        WHERE Objecttype in ('Tab', 'Viw')
                        {database_name_filter}
                        AND ObjectTableName IS NOT NULL
                        AND ObjectColumnName IS NULL
                        -- AND LogDate BETWEEN '2017-01-01' AND '2017-08-01' /* uncomment for PDCR */
                        --	AND LogDate BETWEEN current_date - 90 AND current_date - 1 /* uncomment for PDCR */
                        GROUP BY 1,2,3
                            ) AS QTU1
            INNER JOIN DBC.DBQLogTbl QU /* uncomment for DBC */
            ON QTU1.QueryID=QU.QueryID
            AND (QU.AMPCPUTime + QU.ParserCPUTime) > 0
            {user_name_filter}
    
            GROUP BY 1,2, 3
        ) a
        order by PercentTotal desc
        qualify PercentTotal>0
        ;
    
        """
        logger.debug(f"Tool: handle_dba_tableUsageImpact: table_usage_sql: {table_usage_sql}")
        with conn.cursor() as cur:
            logger.debug("Database version information requested.")
            rows = cur.execute(table_usage_sql.format(database_name_filter=database_name_filter, user_name_filter=user_name_filter))
            data = rows_to_json(cur.description, rows.fetchall())
        if len(data):
            info=f'This data contains the list of tables most frequently queried objects in database schema {database_name}'
        else:
            info=f'No tables have recently been queried in the database schema {database_name}.'
        metadata = {
            "tool_name": "handle_dba_tableUsageImpact",
            "database": database_name,
            "table_count": len(data),
            "comment": info,
            "rows": len(data)
        }
        logger.debug(f"Tool: handle_dba_tableUsageImpact: metadata: {metadata}")
        return create_response(data, metadata)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavior. It implies a read operation but does not confirm read-only nature, access requirements, performance impact, or limitations. The word 'measure' suggests non-destructive action but is not explicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with two sentences and a parameter list, front-loading the purpose. Minimal waste, though the argument list could be integrated more cleanly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool lacks an output schema and has no annotations. The description does not specify return values or format, leaving a gap for agents to understand what the tool outputs. Also, parameter interaction is ambiguous.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description adds basic meaning to parameters ('database name to analyze', 'user name to analyze'). However, it does not clarify behavior when both are null or the relationship between filtering (AND vs OR).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool measures usage of tables and views by users, with a specific goal of identifying resource drivers. However, it does not explicitly differentiate from sibling tools like base_tableUsage or dba_tableSpace.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a use case (understanding resource usage) but lacks guidance on when to use this tool versus alternatives like base_tableUsage or dba_resusageSummary, and no exclusions or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.