{
    "action": "tool_call",
    "reasoning": "My previous draft cited correlations I have not computed in this turn. I need to ground them in query results, so I'll start by checking the distinct Stage values across the pooled tables to build an ordinal mapping.",
    "tool": "query_sql",
    "args": {
        "sql": "WITH u AS (SELECT Stage, Amount, Founded::VARCHAR AS Founded FROM raw.\"Indian Startups _ Funding  Investors Data January 2022\" UNION ALL SELECT Stage, Amount, Founded::VARCHAR FROM raw.\"Indian Startups _ Funding  Investors Data February 2022\" UNION ALL SELECT Stage, Amount, Founded::VARCHAR FROM raw.\"Indian Startups _ Funding  Investors Data March 2022\" UNION ALL SELECT Stage, Amount, Founded::VARCHAR FROM raw.\"Indian Startups _ Funding  Investors Data April 2022\" UNION ALL SELECT Stage, Amount, Founded::VARCHAR FROM raw.\"Indian Startups _ Funding  Investors Data May 2022\") SELECT Stage, COUNT(*) AS n FROM u GROUP BY Stage ORDER BY n DESC",
        "purpose": "List distinct Stage values and counts across all five monthly tables",
        "source": "raw"
    }
}