data-analysis

name	data-analysis
description	Analyze banking data — spending patterns, income vs expenses, savings projections, and financial health metrics
enabled	true

Instructions

When the user asks for spending analysis, budget breakdown, savings projections, or financial insights, follow this workflow:

1. Data Ingestion

• If the user has transaction data from transaction-history, use that as input
• If the user provides CSV/JSON data inline, write it to /tmp first
• If no data is available, use the Faker MCP server to generate realistic sample banking data (transactions, balances, spending patterns). Prefer calling Faker MCP tools directly (e.g., faker_date_between, faker_random_element, faker_pyfloat) over writing inline Python with the faker library.

2. Banking-Specific Analyses

Spending Breakdown

import pandas as pd

df = pd.DataFrame(transactions)
spending = df[df["amount"] < 0].copy()
spending["amount"] = spending["amount"].abs()
breakdown = spending.groupby("category")["amount"].agg(["sum", "count", "mean"])
breakdown.columns = ["Total Spent", "# Transactions", "Avg Transaction"]
breakdown = breakdown.sort_values("Total Spent", ascending=False)
print(breakdown)

Income vs Expenses

income = df[df["amount"] > 0]["amount"].sum()
expenses = df[df["amount"] < 0]["amount"].abs().sum()
net = income - expenses
savings_rate = (net / income * 100) if income > 0 else 0

print(f"Total Income:   ${income:,.2f}")
print(f"Total Expenses: ${expenses:,.2f}")
print(f"Net Cash Flow:  ${net:,.2f}")
print(f"Savings Rate:   {savings_rate:.1f}%")

Monthly Trend

df["month"] = pd.to_datetime(df["date"]).dt.to_period("M")
monthly = df.groupby("month")["amount"].sum()
print(monthly)

Top Merchants

top = spending.groupby("description")["amount"].sum().nlargest(10)
print("Top 10 Merchants by Spend:")
print(top)

3. Visualizations

Generate charts with matplotlib and save to /tmp:

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# Spending by category pie chart
fig, ax = plt.subplots(figsize=(10, 8))
breakdown["Total Spent"].plot(kind="pie", autopct="%1.1f%%", ax=ax)
ax.set_ylabel("")
ax.set_title("Spending by Category")
plt.tight_layout()
plt.savefig("/tmp/spending_breakdown.png", dpi=150)
plt.close()
print("Chart saved: /tmp/spending_breakdown.png")

Common banking charts:

• Pie chart: Spending by category
• Bar chart: Monthly income vs. expenses
• Line chart: Account balance trend over time
• Stacked bar: Category breakdown per month

4. Financial Health Indicators

Provide insights based on the data:

• 50/30/20 Rule Check: Is the user spending ~50% on needs, 30% wants, 20% savings?
• Recurring charges: Identify subscriptions and recurring payments
• Large/unusual transactions: Flag transactions significantly above average
• Savings potential: "Reducing dining by 20% would save ~$X/month"

5. Output

• Print key findings as text
• Save charts to /tmp with descriptive names
• Reference file paths for download
• If analysis produces a transformed dataset, save as CSV to /tmp

Chaining

• code_interpreter — runs the Python code
• transaction-history — provides source transaction data
• file-sharing — delivers charts and exports
• account-lookup — context on account balances

Constraints

• Pre-installed libraries: pandas, numpy, matplotlib, faker. Additional libraries can be installed at runtime via pip.
• Files must be written to /tmp
• Max execution time: 30 seconds per code block