{
    "action": "tool_call",
    "reasoning": "My previous reply cited figures I can't see in the current evidence, so I need to compute them with SQL before answering. I'll run one profiling query over the raw table for row count, distinct User_IDs and per-column non-null counts.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS total_rows, COUNT(DISTINCT User_ID) AS distinct_users, COUNT(Productivity_Score) AS productivity_non_null, COUNT(Accuracy_Rating) AS accuracy_non_null, COUNT(Satisfaction_Score) AS satisfaction_non_null, COUNT(NULLIF(TRIM(AI_Tool), '')) AS ai_tool_non_blank, COUNT(NULLIF(TRIM(AI_Purpose), '')) AS ai_purpose_non_blank, COUNT(NULLIF(TRIM(Gender), '')) AS gender_non_blank, COUNT(DISTINCT Gender) AS gender_variants, COUNT(DISTINCT AI_Tool) AS ai_tool_variants, COUNT(DISTINCT Education_Level) AS education_variants FROM raw.AI_Usage_and_Impact_on_Students_and_Professionals",
        "purpose": "Profile row count, duplicate User_IDs, missing values and category variant counts to ground the data-quality claims",
        "source": "raw"
    }
}