{
    "action": "tool_call",
    "reasoning": "No evidence has been gathered yet, so the numbers in my earlier draft are unverified. I'll compute the Stage distribution across all five monthly tables in one query.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH all_rows AS (SELECT 'Jan' AS month, Stage, Location, Sector FROM raw.\"Indian Startups _ Funding  Investors Data January 2022\" UNION ALL SELECT 'Feb', Stage, Location, Sector FROM raw.\"Indian Startups _ Funding  Investors Data February 2022\" UNION ALL SELECT 'Mar', Stage, Location, Sector FROM raw.\"Indian Startups _ Funding  Investors Data March 2022\" UNION ALL SELECT 'Apr', Stage, Location, Sector FROM raw.\"Indian Startups _ Funding  Investors Data April 2022\" UNION ALL SELECT 'May', Stage, Location, Sector FROM raw.\"Indian Startups _ Funding  Investors Data May 2022\") SELECT COALESCE(NULLIF(TRIM(Stage), ''), '(blank)') AS stage, COUNT(*) AS rows_n FROM all_rows GROUP BY 1 ORDER BY rows_n DESC",
        "purpose": "Stage distribution across all monthly tables to see how records segment by funding stage",
        "source": "raw"
    }
}