{
    "action": "tool_call",
    "reasoning": "No query results are in evidence for the distinct counts I drafted, so I will compute them now with one UNION ALL query over the five monthly tables.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT 'January' AS month, COUNT(*) AS row_count, COUNT(DISTINCT \"Company Name\") AS company_name, COUNT(DISTINCT Location) AS location, COUNT(DISTINCT Sector) AS sector, COUNT(DISTINCT Stage) AS stage, COUNT(DISTINCT Investor) AS investor FROM raw.\"Indian Startups _ Funding  Investors Data January 2022\" UNION ALL SELECT 'February', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding  Investors Data February 2022\" UNION ALL SELECT 'March', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding  Investors Data March 2022\" UNION ALL SELECT 'April', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding  Investors Data April 2022\" UNION ALL SELECT 'May', COUNT(*), COUNT(DISTINCT \"Company Name\"), COUNT(DISTINCT Location), COUNT(DISTINCT Sector), COUNT(DISTINCT Stage), COUNT(DISTINCT Investor) FROM raw.\"Indian Startups _ Funding  Investors Data May 2022\"",
        "purpose": "Distinct counts per categorical column for each monthly table"
    }
}