{
    "action": "tool_call",
    "reasoning": "My previous draft cited NMI scores that aren't in the evidence, so I can't use them. I'll start by profiling the columns I'd relate to each other: distinct counts, nulls, and how much of Amount in USD parses as a number.",
    "tool": "query_sql",
    "args": {
        "sql": "SELECT COUNT(*) AS n, COUNT(DISTINCT \"Industry Vertical\") AS d_industry, COUNT(DISTINCT SubVertical) AS d_sub, COUNT(DISTINCT \"City  Location\") AS d_city, COUNT(DISTINCT InvestmentnType) AS d_type, COUNT(DISTINCT Remarks) AS d_remarks, COUNT(DISTINCT \"Startup Name\") AS d_startup, COUNT(\"Industry Vertical\") AS nn_industry, COUNT(SubVertical) AS nn_sub, COUNT(\"City  Location\") AS nn_city, COUNT(InvestmentnType) AS nn_type, COUNT(Remarks) AS nn_remarks, COUNT(\"Amount in USD\") AS nn_amount, COUNT(TRY_CAST(REPLACE(\"Amount in USD\", ',', '') AS DOUBLE)) AS parsable_amount, MEDIAN(TRY_CAST(REPLACE(\"Amount in USD\", ',', '') AS DOUBLE)) AS median_amount FROM raw.startup_funding",
        "purpose": "Profile cardinality, null rates and amount parseability before choosing how to relate columns",
        "source": "raw"
    }
}