{
    "action": "tool_call",
    "reasoning": "My earlier reply was prose and was not backed by any query I can cite. I'll first profile the main columns with one query: row count, 'nan' and 'N/A' rates, and distinct counts. Later claims can then rest on this result.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS total_rows, COUNT(DISTINCT \"Startup Name\") AS distinct_startups, COUNT(DISTINCT \"City  Location\") AS distinct_cities, COUNT(DISTINCT \"Industry Vertical\") AS distinct_industries, COUNT(DISTINCT \"InvestmentnType\") AS distinct_round_types, SUM(CASE WHEN \"Amount in USD\" IS NULL OR \"Amount in USD\" IN ('N/A','nan','') THEN 1 ELSE 0 END) AS missing_amount, SUM(CASE WHEN \"Industry Vertical\" IN ('nan','N/A') OR \"Industry Vertical\" IS NULL THEN 1 ELSE 0 END) AS missing_industry, SUM(CASE WHEN \"SubVertical\" IN ('nan','N/A') OR \"SubVertical\" IS NULL THEN 1 ELSE 0 END) AS missing_subvertical, SUM(CASE WHEN \"City  Location\" IN ('nan','N/A') OR \"City  Location\" IS NULL THEN 1 ELSE 0 END) AS missing_city, SUM(CASE WHEN \"Remarks\" IN ('nan','N/A') OR \"Remarks\" IS NULL THEN 1 ELSE 0 END) AS missing_remarks FROM raw.startup_funding",
        "purpose": "Profile row counts, distinct counts and missing-value rates across key columns",
        "source": "raw"
    }
}