{
    "action": "tool_call",
    "reasoning": "Profile the format patterns of the Amount column (VARCHAR) across all five monthly tables in one query, to see how amounts are written before parsing them.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH a AS (SELECT 'Jan' AS m, \"Company Name\" AS c, Amount, Stage FROM raw.\"Indian Startups _ Funding  Investors Data January 2022\" UNION ALL SELECT 'Feb', \"Company Name\", Amount, Stage FROM raw.\"Indian Startups _ Funding  Investors Data February 2022\" UNION ALL SELECT 'Mar', \"Company Name\", Amount, Stage FROM raw.\"Indian Startups _ Funding  Investors Data March 2022\" UNION ALL SELECT 'Apr', \"Company Name\", Amount, Stage FROM raw.\"Indian Startups _ Funding  Investors Data April 2022\" UNION ALL SELECT 'May', \"Company Name\", Amount, Stage FROM raw.\"Indian Startups _ Funding  Investors Data May 2022\") SELECT regexp_replace(regexp_replace(coalesce(Amount,'<NULL>'),'[0-9]+','9','g'),'[A-Za-z]+','a','g') AS amount_pattern, COUNT(*) AS n, MIN(Amount) AS ex_min, MAX(Amount) AS ex_max, COUNT(DISTINCT m) AS months FROM a GROUP BY 1 ORDER BY n DESC",
        "purpose": "Profile Amount format patterns across all months",
        "source": "raw"
    }
}