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by Teradata

dba_userSqlList

Retrieve SQL queries executed by a specific user or all users within a specified timeframe to monitor database activity and analyze query history.

Instructions

Get a list of SQL run by a user in the last number of days if a user name is provided, otherwise get list of all SQL in the last number of days.

Arguments: user_name - user name no_days - number of days

Returns: ResponseType: formatted response with query results + metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_nameYes
no_daysNo

Implementation Reference

  • Handler function executing the dba_userSqlList tool: retrieves SQL queries run by a specific user or all users in the last N days from Teradata's query logs.
    def handle_dba_userSqlList(conn: TeradataConnection, user_name: str, no_days: int | None = 7,  *args, **kwargs):
        """
        Get a list of SQL run by a user in the last number of days if a user name is provided, otherwise get list of all SQL in the last number of days.
    
        Arguments:
          user_name - user name
          no_days - number of days
    
        Returns:
          ResponseType: formatted response with query results + metadata
        """
        logger.debug(f"Tool: handle_dba_userSqlList: Args: user_name: {user_name}")
    
        with conn.cursor() as cur:
            if user_name == "":
                logger.debug("No user name provided, returning all SQL queries.")
                rows = cur.execute(f"""SELECT t1.QueryID, t1.ProcID, t1.CollectTimeStamp, t1.SqlTextInfo, t2.UserName
                FROM DBC.QryLogSqlV t1
                JOIN DBC.QryLogV t2
                ON t1.QueryID = t2.QueryID
                WHERE t1.CollectTimeStamp >= CURRENT_TIMESTAMP - INTERVAL '{no_days}' DAY
                ORDER BY t1.CollectTimeStamp DESC;""")
            else:
                logger.debug(f"User name provided: {user_name}, returning SQL queries for this user.")
                rows = cur.execute(f"""SELECT t1.QueryID, t1.ProcID, t1.CollectTimeStamp, t1.SqlTextInfo, t2.UserName
                FROM DBC.QryLogSqlV t1
                JOIN DBC.QryLogV t2
                ON t1.QueryID = t2.QueryID
                WHERE t1.CollectTimeStamp >= CURRENT_TIMESTAMP - INTERVAL '{no_days}' DAY
                AND t2.UserName = '{user_name}'
                ORDER BY t1.CollectTimeStamp DESC;""")
            data = rows_to_json(cur.description, rows.fetchall())
            metadata = {
                "tool_name": "dba_userSqlList",
                "user_name": user_name,
                "no_days": no_days,
                "total_queries": len(data)
            }
            logger.debug(f"Tool: handle_dba_userSqlList: metadata: {metadata}")
            return create_response(data, metadata)
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It states the tool retrieves SQL lists (implying read-only) and mentions the return format ('formatted response with query results + metadata'), but lacks details on permissions, rate limits, pagination, or error handling. For a tool with no annotations, this leaves significant behavioral gaps, though it does add some value beyond the schema.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by structured sections for arguments and returns. There's minimal waste, though the 'Arguments' and 'Returns' labels could be integrated more fluidly. Every sentence adds value, making it efficient.

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

Completeness3/5

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

Given no annotations, 0% schema coverage, and no output schema, the description is moderately complete. It covers the purpose, parameters, and return format, but lacks details on behavioral traits (e.g., permissions, limits) and doesn't fully compensate for the missing schema descriptions. For a tool with 2 parameters and conditional logic, it's adequate but has clear gaps.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It adds meaning by explaining 'user_name' as 'user name' and 'no_days' as 'number of days', and clarifies the conditional logic (user-specific vs. all SQL). However, it doesn't specify format constraints (e.g., date ranges, user name validation) or the default value for 'no_days' (which is in the schema), leaving minor gaps.

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's purpose: 'Get a list of SQL run by a user in the last number of days if a user name is provided, otherwise get list of all SQL in the last number of days.' This specifies the verb ('Get a list'), resource ('SQL run'), and conditional logic. However, it doesn't explicitly differentiate from sibling tools like 'dba_tableSqlList' or 'sql_Retrieve_Cluster_Queries', which might offer similar SQL-related queries.

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 implies usage context through conditional logic (user-specific vs. all SQL) but doesn't provide explicit guidance on when to use this tool versus alternatives. It mentions no prerequisites, exclusions, or comparisons to sibling tools like 'dba_tableSqlList' or 'sql_Retrieve_Cluster_Queries', leaving the agent to infer usage based on the description alone.

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

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