# Install required packages
pip install anthropic mcp pandas

# Verify installation
python -c "import mcp; print('MCP SDK ready')"

import asyncio
import pandas as pd
import anthropic
from pathlib import Path
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp import types

# Initialize server
app = Server('csv-analyst-server')
DATA_FOLDER = Path('./data')
DATA_FOLDER.mkdir(exist_ok=True)

@app.list_tools()
async def list_tools() -> list[types.Tool]:
    return [
        types.Tool(
            name='list_data_files',
            description='List all available CSV data files in the data folder.',
            inputSchema={'type': 'object', 'properties': {}, 'required': []}
        ),
        types.Tool(
            name='query_csv',
            description='Answer a question about a CSV file using data analysis.',
            inputSchema={
                'type': 'object',
                'properties': {
                    'filename': {'type': 'string', 'description': 'Name of the CSV file'},
                    'question': {'type': 'string', 'description': 'Question to answer about the data'}
                },
                'required': ['filename', 'question']
            }
        )
    ]

@app.call_tool()
async def call_tool(name: str, arguments: dict) -> list[types.TextContent]:
    if name == 'list_data_files':
        files = list(DATA_FOLDER.glob('*.csv'))
        if not files:
            return [types.TextContent(type='text', text='No CSV files found.')]
        file_list = '\n'.join([f.name for f in files])
        return [types.TextContent(type='text', text=f'Available files:\n{file_list}')]

    elif name == 'query_csv':
        filepath = DATA_FOLDER / arguments['filename']
        if not filepath.exists():
            return [types.TextContent(type='text', text=f'File not found: {arguments["filename"]}')]

        df = pd.read_csv(filepath)
        summary = f"""File: {arguments['filename']}
Rows: {len(df)} | Columns: {list(df.columns)}
Sample: {df.head().to_string()}
Stats:  {df.describe().to_string()}"""

        client = anthropic.Anthropic()
        response = client.messages.create(
            model='claude-sonnet-4-20250514',
            max_tokens=500,
            messages=[{'role': 'user',
                       'content': f'Data:\n{summary}\nAnswer: {arguments["question"]}'}]
        )
        answer = response.content[0].text
        return [types.TextContent(type='text', text=answer)]

    return [types.TextContent(type='text', text='Unknown tool')]

async def main():
    async with stdio_server() as (read_stream, write_stream):
        await app.run(read_stream, write_stream, app.create_initialization_options())

if __name__ == '__main__':
    asyncio.run(main())

import pandas as pd
import os

os.makedirs('data', exist_ok=True)

# Create sample sales data
df = pd.DataFrame({
    'month':       ['Jan','Feb','Mar','Apr','May','Jun'],
    'revenue_usd': [45000,52000,48000,61000,58000,73000],
    'customers':   [120,145,133,178,162,201],
    'product':     ['Course','Course','Course',
                   'Course+Consulting','Course','Course+Consulting']
})
df.to_csv('data/sales_2026.csv', index=False)
print('Sample data created: data/sales_2026.csv')

{
  "mcpServers": {
    "csv-analyst": {
      "command": "python",
      "args": ["/full/path/to/your/mcp_server.py"],
      "env": {
        "ANTHROPIC_API_KEY": "your-api-key-here"
      }
    }
  }
}