Math MCP Learning

Educational MCP server with math operations, statistics, visualizations, and persistent workspace.

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What it can do

  • Calculate: Safely evaluate mathematical expressions with support for basic operations and math functions. Supported operations: +, -, *, /, **, () Supported functions: sin, cos, tan, log, sqrt, abs, p
  • Statistics: 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 numb
  • Compound Interest: 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 compou

What data it sees

Do you need an account

No: the server works without sign-in

Educational MCP server with math operations, statistics, visualizations, and persistent workspace.

Server tool list (17)

Raw names from tools/list. Only developers need these.

calculateSafely 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
statisticsPerform 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
compound_interestCalculate 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_unitsConvert 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
matrix_multiplyMultiply two matrices (A × B). Args: matrix_a: First matrix (m x n) matrix_b: Second matrix (n x p) 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]])
matrix_transposeTranspose a matrix (swap rows and columns). Args: matrix: Input matrix (m x n) Note: Requires NumPy. Raises ValueError if NumPy is unavailable. Examples: matrix_transpose([[1, 2, 3], [4, 5, 6]]) matrix_transpose([[1], [2], [3]])
matrix_determinantCalculate the determinant of a square matrix. Args: matrix: Square matrix (n x n) 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
matrix_inverseCalculate the inverse of a square matrix. Args: matrix: Square matrix (n x n) Note: Requires NumPy. Raises ValueError if NumPy is unavailable. Examples: matrix_inverse([[1, 2], [3, 4]]) matrix_inverse([[2, 0], [0, 2]]) # Diagonal matrix
matrix_eigenvaluesCalculate the eigenvalues of a square matrix. Args: matrix: Square matrix (n x n) 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_calculationSave 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_variableLoad previously saved calculation result from workspace. Examples: load_variable("portfolio_return") # Returns saved calculation load_variable("circle_area") # Access across sessions
plot_functionGenerate mathematical function plots (requires matplotlib). Args: expression: Mathematical expression to plot (e.g., "x**2", "sin(x)") x_range: Tuple of (min, max) for x-axis range num_points: Number of points to plot (default: 100) ctx: FastMCP context for logging Examples: plot_function("x**2", (-5, 5)) plot_function("sin(x)", (-3.14, 3.14))
create_histogramCreate statistical histograms (requires matplotlib). Args: data: List of numerical values bins: Number of histogram bins (default: 20) title: Chart title ctx: FastMCP context for logging Examples: create_histogram([1.0, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0]) create_histogram([10, 20, 30, 40, 50], bins=5, title="Test Scores")
plot_line_chartCreate a line chart from data points (requires matplotlib). Args: x_data: X-axis data points y_data: Y-axis data points title: Chart title x_label: X-axis label y_label: Y-axis label color: Line color (name or hex code, e.g., 'blue', '#2E86AB') show_grid: Whether to show grid lines ctx: FastMCP context for logging 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')
plot_scatter_chartCreate a scatter plot from data points (requires matplotlib). Args: x_data: X-axis data points y_data: Y-axis data points title: Chart title x_label: X-axis label y_label: Y-axis label color: Point color (name or hex code, e.g., 'blue', '#2E86AB') point_size: Size of scatter points (default: 50) ctx: FastMCP context for logging Examples: plot_scatter_chart([1, 2, 3, 4], [1, 4, 9, 16], title="Correlation Study") plot_scatter_chart([1, 2, 3], [2, 4, 5], color='purple', point_size=100)
plot_box_plotCreate a box plot for comparing distributions (requires matplotlib). Args: data_groups: List of data groups to compare group_labels: Optional labels for each group title: Chart title y_label: Y-axis label color: Box color (name or hex code, e.g., 'blue', '#2E86AB') ctx: FastMCP context for logging 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")
plot_financial_lineGenerate and plot synthetic financial price data (requires matplotlib). Creates realistic price movement patterns for educational purposes. Does not use real market data. Args: days: Number of days to generate (default: 30) trend: Market trend ('bullish', 'bearish', or 'volatile') start_price: Starting price value (default: 100.0) color: Line color (name or hex code, e.g., 'blue', '#2E86AB') ctx: FastMCP context for logging 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')