Safely evaluate mathematical expressions with support for basic operations and math functions.

Supported operations: +, -, *, /, **, () Supported functions: sin, cos, tan, log, sqrt, abs, pow

Note: Use this tool to evaluate a single mathematical expression. To compute descriptive statistics over a list of numbers, use the statistics tool instead.

Examples:

• "2 + 3 * 4" → 14

• "sqrt(16)" → 4.0

• "sin(3.14159/2)" → 1.0

Perform statistical calculations on a list of numbers.

Available operations: mean, median, mode, std_dev, variance

Note: Use this tool to compute descriptive statistics over a list of numbers. To evaluate a single mathematical expression, use the calculate tool instead.

Examples: statistics([1.0, 2.5, 3.0, 4.5, 5.0], "mean") # Returns 3.2 statistics([1.0, 2.5, 3.0, 4.5, 5.0], "std_dev") # Returns ~1.58

Calculate compound interest for investments.

Formula: A = P(1 + r/n)^(nt) Where:

• P = principal amount

• r = annual interest rate (as decimal)

• n = number of times interest compounds per year

• t = time in years

Examples: compound_interest(10000, 0.05, 5) # $10,000 at 5% for 5 years → $12,762.82 compound_interest(5000, 0.03, 10, 12) # $5,000 at 3% compounded monthly → $6,744.25

Convert between different units of measurement.

Supported unit types:

• length: mm, cm, m, km, in, ft, yd, mi

• weight: g, kg, oz, lb

• temperature: c, f, k (Celsius, Fahrenheit, Kelvin)

Examples: convert_units(5, "km", "mi", "length") # 5 kilometers → 3.11 miles convert_units(150, "lb", "kg", "weight") # 150 pounds → 68.04 kilograms

Multiply two matrices (A × B).

Note: Requires NumPy. Raises ValueError if NumPy is unavailable.

Examples: matrix_multiply([[1, 2], [3, 4]], [[5, 6], [7, 8]]) matrix_multiply([[1, 2, 3]], [[1], [2], [3]])

Transpose a matrix (swap rows and columns).

Note: Requires NumPy. Raises ValueError if NumPy is unavailable.

Examples: matrix_transpose([[1, 2, 3], [4, 5, 6]]) matrix_transpose([[1], [2], [3]])

Calculate the determinant of a square matrix.

Note: Requires NumPy. Raises ValueError if NumPy is unavailable.

Examples: matrix_determinant([[1, 2], [3, 4]]) matrix_determinant([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) # Identity matrix

Calculate the inverse of a square matrix.

Note: Requires NumPy. Raises ValueError if NumPy is unavailable.

Examples: matrix_inverse([[1, 2], [3, 4]]) matrix_inverse([[2, 0], [0, 2]]) # Diagonal matrix

Calculate the eigenvalues of a square matrix.

Note: Requires NumPy. Raises ValueError if NumPy is unavailable.

Examples: matrix_eigenvalues([[4, 2], [1, 3]]) matrix_eigenvalues([[3, 0, 0], [0, 5, 0], [0, 0, 7]]) # Diagonal matrix

Save calculation to persistent workspace (survives restarts).

Examples: save_calculation("portfolio_return", "10000 * 1.07^5", 14025.52) save_calculation("circle_area", "pi * 5^2", 78.54)

Load previously saved calculation result from workspace.

Examples: load_variable("portfolio_return") # Returns saved calculation load_variable("circle_area") # Access across sessions

Generate mathematical function plots (requires matplotlib).

Examples: plot_function("x**2", (-5, 5)) plot_function("sin(x)", (-3.14, 3.14))

Create statistical histograms (requires matplotlib).

Examples: plot_histogram([1.0, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0]) plot_histogram([10, 20, 30, 40, 50], bins=5, title="Test Scores")

Create a line chart from data points (requires matplotlib).

Note: Use for general XY data. For time-series price data with optional moving average, use plot_financial_line instead.

Examples: plot_line_chart([1, 2, 3, 4], [1, 4, 9, 16], title="Squares") plot_line_chart([0, 1, 2], [0, 1, 4], color='red', x_label='Time', y_label='Distance')

Create a scatter plot from data points (requires matplotlib).

Examples: plot_scatter([1, 2, 3, 4], [1, 4, 9, 16], title="Correlation Study") plot_scatter([1, 2, 3], [2, 4, 5], color='purple', point_size=100)

Create a box plot for comparing distributions (requires matplotlib).

Examples: plot_box_plot([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10]], group_labels=["A", "B"]) plot_box_plot([[10, 20, 30], [15, 25, 35], [5, 15, 25]], title="Comparison")

Generate and plot synthetic financial price data (requires matplotlib).

Creates realistic price movement patterns for educational purposes. Does not use real market data.

Note: Use for time-series price data with optional moving average overlay. For general XY data, use plot_line_chart instead.

Examples: plot_financial_line(days=60, trend='bullish') plot_financial_line(days=90, trend='volatile', start_price=150.0, color='orange')