Math MCP Learning

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

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

Examples:

• "2 + 3 * 4" → 14

• "sqrt(16)" → 4.0

• "sin(3.14159/2)" → 1.0

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

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)

Create 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

Returns: Dict with base64-encoded PNG image or error message

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")

Load previously saved calculation result from workspace.

Args: name: Variable name to load

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

Calculate the determinant of a square matrix.

Args: matrix: Square matrix (n x n)

Returns: Determinant value (scalar)

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

Calculate the eigenvalues of a square matrix.

Args: matrix: Square matrix (n x n)

Returns: List of eigenvalues (may be complex numbers)

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

Calculate the inverse of a square matrix.

Args: matrix: Square matrix (n x n)

Returns: Inverse matrix (n x n)

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

Multiply two matrices (A × B).

Args: matrix_a: First matrix (m x n) matrix_b: Second matrix (n x p)

Returns: Result matrix (m x p)

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).

Args: matrix: Input matrix (m x n)

Returns: Transposed matrix (n x m)

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

Create 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

Returns: Dict with base64-encoded PNG image or error message

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.

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

Returns: Dict with base64-encoded PNG image or error message

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

Generate 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

Returns: Image object with PNG data or VisualizationError on failure

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

Create 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

Returns: Dict with base64-encoded PNG image or error message

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).

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

Returns: Dict with base64-encoded PNG image or error message

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)

Save calculation to persistent workspace (survives restarts).

Args: name: Variable name to save under expression: The mathematical expression result: The calculated result

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

Perform statistical calculations on a list of numbers.

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