{
    "action": "tool_call",
    "reasoning": "No query results are in evidence in this turn, so I can't rely on the figures in my unparsed draft. I'll re-ground the answer by computing monthly round counts and Amount quality across the five tables in one query.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT 'Jan' AS month, COUNT(*) AS rows_total, COUNT(\"Company Name\") AS with_company, COUNT(Amount) AS with_amount, SUM(CASE WHEN Amount ILIKE '%undisclosed%' THEN 1 ELSE 0 END) AS undisclosed FROM raw.\"Indian Startups _ Funding  Investors Data January 2022\" UNION ALL SELECT 'Feb', COUNT(*), COUNT(\"Company Name\"), COUNT(Amount), SUM(CASE WHEN Amount ILIKE '%undisclosed%' THEN 1 ELSE 0 END) FROM raw.\"Indian Startups _ Funding  Investors Data February 2022\" UNION ALL SELECT 'Mar', COUNT(*), COUNT(\"Company Name\"), COUNT(Amount), SUM(CASE WHEN Amount ILIKE '%undisclosed%' THEN 1 ELSE 0 END) FROM raw.\"Indian Startups _ Funding  Investors Data March 2022\" UNION ALL SELECT 'Apr', COUNT(*), COUNT(\"Company Name\"), COUNT(Amount), SUM(CASE WHEN Amount ILIKE '%undisclosed%' THEN 1 ELSE 0 END) FROM raw.\"Indian Startups _ Funding  Investors Data April 2022\" UNION ALL SELECT 'May', COUNT(*), COUNT(\"Company Name\"), COUNT(Amount), SUM(CASE WHEN Amount ILIKE '%undisclosed%' THEN 1 ELSE 0 END) FROM raw.\"Indian Startups _ Funding  Investors Data May 2022\"",
        "purpose": "Monthly round counts and Amount completeness across the five monthly tables",
        "source": "raw"
    }
}