Stripe MCP Agent

# .env
STRIPE_SECRET_KEY=sk_test_xxxxxxxxxxxxxxxxxxxx
ANTHROPIC_API_KEY=your-anthropic-key-here

# With uv (recommended)
uv pip install upsonic python-dotenv

# With pip
pip install upsonic python-dotenv

import os
from dotenv import load_dotenv
from upsonic import AutonomousAgent, Task
from upsonic.tools.mcp import MCPHandler

load_dotenv()

stripe_handler = MCPHandler(
    command="npx -y @stripe/mcp --tools=all",
    env={
        "STRIPE_SECRET_KEY": os.getenv("STRIPE_SECRET_KEY")
    },
    timeout_seconds=60
)

agent = AutonomousAgent(
    model="anthropic/claude-sonnet-4-6",
    workspace="./revenue-reports",
    tools=[stripe_handler]
)

task = Task(
    description="""
    Generate a monthly revenue report from Stripe data.

    1. Fetch all successful payments from the last 30 days
    2. Fetch all active subscriptions

    Write the following files:
    - 'payments.csv' with columns: date, customer_email, amount, currency,
      product, payment_method
    - 'revenue_report.md' with sections:
      * Total Revenue (sum of all payments)
      * Revenue by Product (table)
      * Payment Method Distribution (card vs bank transfer vs other)
      * Monthly Recurring Revenue (MRR) from active subscriptions
      * Top 10 Customers by Spend
    """
)

agent.print_do(task)

import os
from dotenv import load_dotenv
from upsonic import AutonomousAgent, Task
from upsonic.tools.mcp import MCPHandler

load_dotenv()

stripe_handler = MCPHandler(
    command="npx -y @stripe/mcp --tools=all",
    env={
        "STRIPE_SECRET_KEY": os.getenv("STRIPE_SECRET_KEY")
    },
    timeout_seconds=60
)

agent = AutonomousAgent(
    model="anthropic/claude-sonnet-4-6",
    workspace="./analytics",
    tools=[stripe_handler]
)

task = Task(
    description="""
    Analyze subscription churn from Stripe data.

    1. Fetch all subscriptions (active, canceled, and past_due)
    2. For canceled subscriptions, note the cancellation date and plan

    Write the following files:
    - 'subscriptions.csv' with columns: customer_email, plan, status,
      start_date, cancel_date, monthly_amount
    - 'churn_analysis.md' with:
      * Active vs Canceled vs Past Due counts
      * Churn rate (canceled / total)
      * Most churned plan (which plan loses the most subscribers)
      * At-risk subscriptions (past_due) with customer details
      * Recommendations for reducing churn
    """
)

agent.print_do(task)