# Required: Snowflake Connection
# Account examples:
# - ACCOUNT-LOCATOR.snowflakecomputing.com (recommended)
# - ACCOUNT-LOCATOR.region.cloud
# - organization-account_name
SNOWFLAKE_ACCOUNT=ACCOUNT-LOCATOR.snowflakecomputing.com
SNOWFLAKE_USER=your_username
SNOWFLAKE_PASSWORD=your_password

# Optional: Default Snowflake Context
SNOWFLAKE_WAREHOUSE=your_warehouse_name
SNOWFLAKE_DATABASE=your_database_name
SNOWFLAKE_SCHEMA=your_schema_name
SNOWFLAKE_ROLE=your_role_name

# Required: OpenAI API
OPENAI_API_KEY=your_openai_api_key
OPENAI_MODEL=gpt-4  # Optional, defaults to gpt-4

python -m src.main

fastmcp run src/main.py

fastmcp dev src/main.py

{
  "mcpServers": {
    "datapilot": {
      "command": "python",
      "args": ["-m", "src.main"],
      "cwd": "/path/to/datapilot",
      "env": {
        "SNOWFLAKE_ACCOUNT": "your_account",
        "SNOWFLAKE_USER": "your_user",
        "SNOWFLAKE_PASSWORD": "your_password",
        "OPENAI_API_KEY": "your_openai_key"
      }
    }
  }
}

from fastmcp import Client

async def main():
    async with Client("python -m src.main") as client:
        # List databases
        databases = await client.call_tool("list_databases")
        print("Databases:", databases)
        
        # Natural language to SQL
        result = await client.call_tool("natural_language_to_sql", {
            "question": "Show me the top 10 customers by revenue",
            "database": "SALES_DB",
            "schema": "PUBLIC"
        })
        print("Generated SQL:", result)

# Ask a question in natural language
question = "What are the top 5 products by sales volume last month?"
sql = await client.call_tool("natural_language_to_sql", {
    "question": question,
    "database": "SALES_DB",
    "schema": "PUBLIC"
})
print(f"Generated SQL: {sql}")

# Execute a query and get AI analysis
analysis = await client.call_tool("analyze_query_results", {
    "query": "SELECT product_name, SUM(quantity) as total_sales FROM sales GROUP BY product_name ORDER BY total_sales DESC LIMIT 10",
    "results_limit": 100,
    "analysis_type": "summary"
})
print(f"Analysis: {analysis}")

# Get AI-powered insights about a table
insights = await client.call_tool("generate_table_insights", {
    "table_name": "SALES_DB.PUBLIC.CUSTOMERS",
    "sample_limit": 50
})
print(f"Table insights: {insights}")

# Get optimization suggestions
optimizations = await client.call_tool("suggest_query_optimizations", {
    "query": "SELECT * FROM large_table WHERE date_column > '2023-01-01'"
})
print(f"Optimization suggestions: {optimizations}")

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   MCP Client    │    │   FastMCP       │    │   Snowflake     │
│   (Claude/etc)  │◄──►│   Server        │◄──►│   Database      │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │   OpenAI API    │
                       │   (GPT-4)       │
                       └─────────────────┘

datapilot/
├── src/
│   ├── __init__.py
│   ├── main.py              # Main FastMCP server
│   ├── models.py            # Pydantic data models
│   ├── snowflake_client.py  # Snowflake connection & operations
│   └── openai_client.py     # OpenAI integration
├── requirements.txt         # Python dependencies
├── env.template            # Environment variables template
└── README.md              # This file

@mcp.tool()
async def my_new_tool(param: str, ctx: Context) -> str:
    """Description of what the tool does"""
    await ctx.info(f"Processing: {param}")
    # Your logic here
    return "result"

@mcp.resource("snowflake://my-resource/{param}")
async def my_resource(param: str) -> Dict[str, Any]:
    """Resource description"""
    # Your logic here
    return {"data": "value"}

LOG_LEVEL=DEBUG