Math MCP Learning

Official17 toolsLive

by clouatre-labs · Python

Educational MCP server with 17 math/stats tools, visualizations, and persistent workspace

io.github.clouatre-labs/math-mcp-learning-server (MCP)

The io.github.clouatre-labs/math-mcp-learning-server is an educational MCP server for math and statistics. It exposes 17 tools for calculations and visualizations, and provides a persistent workspace for ongoing learning sessions. The project targets developers building with MCP and FastMCP in Python.

🛠️ Key Features

17 math/stats tools (toolCount: 17)
Visualizations
Persistent workspace
Labeled educational server in its description

🚀 Use Cases

Learning math and statistics via tool-based interactions
Generating visual outputs alongside calculations
Continuing work across sessions using a persistent workspace

⚡ Developer Benefits

MCP server focused on math/statistics tooling
Python-oriented project
Topics include mcp, mcp-server, fastmcp, and fastmcp-3

⚠️ Limitations

No additional tool names, specific capabilities, or workspace details are provided in the available source excerpt

Topics

calculatoreducationalfastmcpmathematicsmcpmcp-serverpythontutorialfastmcp-3

Captured live from the server via tools/list.

calc_expression

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

Parameters1

expression

string

required

Mathematical expression to evaluate. Supports +, -, *, /, **, and math functions (sin, cos, sqrt, log, etc.). Example: '2 * sin(pi/4) + sqrt(16)'

Raw schema

{
  "type": "object",
  "properties": {
    "expression": {
      "description": "Mathematical expression to evaluate. Supports +, -, *, /, **, and math functions (sin, cos, sqrt, log, etc.). Example: '2 * sin(pi/4) + sqrt(16)'",
      "maxLength": 500,
      "type": "string"
    }
  },
  "required": [
    "expression"
  ],
  "additionalProperties": false
}

calc_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 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

Parameters2

numbers	array	required	List of numbers to compute descriptive statistics on. Example: [1.0, 2.5, 3.0, 4.5, 5.0]
operation	string	required	Statistical operation to perform. Allowed values: mean, median, mode, std_dev, variance

Raw schema

{
  "type": "object",
  "properties": {
    "numbers": {
      "description": "List of numbers to compute descriptive statistics on. Example: [1.0, 2.5, 3.0, 4.5, 5.0]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "operation": {
      "description": "Statistical operation to perform. Allowed values: mean, median, mode, std_dev, variance",
      "examples": [
        "mean",
        "median",
        "mode",
        "std_dev",
        "variance"
      ],
      "type": "string"
    }
  },
  "required": [
    "numbers",
    "operation"
  ],
  "additionalProperties": false
}

calc_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 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

Parameters4

principal	number	required	Initial investment amount in dollars (must be > 0), e.g. 1000.0
rate	number	required	Annual interest rate as decimal 0.0-1.0 (e.g. 0.05 = 5%). If entering a percentage, divide by 100 first.
time	number	required	Investment time in years (must be > 0), e.g. 10.0
compounds_per_year	integer	optional	Compounding frequency per year (must be > 0): 12=monthly, 365=daily

Raw schema

{
  "type": "object",
  "properties": {
    "principal": {
      "description": "Initial investment amount in dollars (must be > 0), e.g. 1000.0",
      "exclusiveMinimum": 0,
      "type": "number"
    },
    "rate": {
      "description": "Annual interest rate as decimal 0.0-1.0 (e.g. 0.05 = 5%). If entering a percentage, divide by 100 first.",
      "maximum": 1,
      "minimum": 0,
      "type": "number"
    },
    "time": {
      "description": "Investment time in years (must be > 0), e.g. 10.0",
      "exclusiveMinimum": 0,
      "type": "number"
    },
    "compounds_per_year": {
      "default": 12,
      "description": "Compounding frequency per year (must be > 0): 12=monthly, 365=daily",
      "exclusiveMinimum": 0,
      "type": "integer"
    }
  },
  "required": [
    "principal",
    "rate",
    "time"
  ],
  "additionalProperties": false
}

calc_units

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

Parameters4

value	number	required	Numeric value to convert, e.g., 100.0
from_unit	string	required	Source unit abbreviation. Valid units depend on unit_type: length (mm, cm, m, km, in, ft, yd, mi), weight (g, kg, oz, lb), temperature (c, f, k)
to_unit	string	required	Target unit abbreviation. Valid units depend on unit_type: length (mm, cm, m, km, in, ft, yd, mi), weight (g, kg, oz, lb), temperature (c, f, k)
unit_type	string	required	Unit category: length, weight, or temperature

Raw schema

{
  "type": "object",
  "properties": {
    "value": {
      "description": "Numeric value to convert, e.g., 100.0",
      "type": "number"
    },
    "from_unit": {
      "description": "Source unit abbreviation. Valid units depend on unit_type: length (mm, cm, m, km, in, ft, yd, mi), weight (g, kg, oz, lb), temperature (c, f, k)",
      "examples": [
        "m",
        "kg",
        "c"
      ],
      "type": "string"
    },
    "to_unit": {
      "description": "Target unit abbreviation. Valid units depend on unit_type: length (mm, cm, m, km, in, ft, yd, mi), weight (g, kg, oz, lb), temperature (c, f, k)",
      "examples": [
        "ft",
        "lb",
        "f"
      ],
      "type": "string"
    },
    "unit_type": {
      "description": "Unit category: length, weight, or temperature",
      "examples": [
        "length",
        "weight",
        "temperature"
      ],
      "type": "string"
    }
  },
  "required": [
    "value",
    "from_unit",
    "to_unit",
    "unit_type"
  ],
  "additionalProperties": false
}

matrix_multiply

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]])

Parameters2

matrix_a	array	required	2D list of numbers representing the first matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]
matrix_b	array	required	2D list of numbers representing the second matrix. Each inner list is a row. Example: [[5, 6], [7, 8]]

Raw schema

{
  "type": "object",
  "properties": {
    "matrix_a": {
      "description": "2D list of numbers representing the first matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "matrix_b": {
      "description": "2D list of numbers representing the second matrix. Each inner list is a row. Example: [[5, 6], [7, 8]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    }
  },
  "required": [
    "matrix_a",
    "matrix_b"
  ],
  "additionalProperties": false
}

matrix_transpose

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]])

Parameters1

matrix

array

required

2D list of numbers representing the matrix. Each inner list is a row. Example: [[1, 2, 3], [4, 5, 6]]

Raw schema

{
  "type": "object",
  "properties": {
    "matrix": {
      "description": "2D list of numbers representing the matrix. Each inner list is a row. Example: [[1, 2, 3], [4, 5, 6]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    }
  },
  "required": [
    "matrix"
  ],
  "additionalProperties": false
}

matrix_determinant

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

Parameters1

matrix

array

required

2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]

Raw schema

{
  "type": "object",
  "properties": {
    "matrix": {
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    }
  },
  "required": [
    "matrix"
  ],
  "additionalProperties": false
}

matrix_inverse

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

Parameters1

matrix

array

required

2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]

Raw schema

{
  "type": "object",
  "properties": {
    "matrix": {
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    }
  },
  "required": [
    "matrix"
  ],
  "additionalProperties": false
}

matrix_eigenvalues

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

Parameters1

matrix

array

required

2D list of numbers representing a square matrix. Each inner list is a row. Example: [[4, 2], [1, 3]]

Raw schema

{
  "type": "object",
  "properties": {
    "matrix": {
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[4, 2], [1, 3]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 10000,
      "type": "array"
    }
  },
  "required": [
    "matrix"
  ],
  "additionalProperties": false
}

workspace_save

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)

Parameters3

name	string	required	Variable name for the saved calculation. Used to retrieve it later. Example: 'circle_area'
expression	string	required	The mathematical expression that was evaluated. Example: 'pi * r**2'
result	number	required	Numeric result of evaluating the expression, e.g., 78.54

Raw schema

{
  "type": "object",
  "properties": {
    "name": {
      "description": "Variable name for the saved calculation. Used to retrieve it later. Example: 'circle_area'",
      "maxLength": 50,
      "type": "string"
    },
    "expression": {
      "description": "The mathematical expression that was evaluated. Example: 'pi * r**2'",
      "maxLength": 500,
      "type": "string"
    },
    "result": {
      "description": "Numeric result of evaluating the expression, e.g., 78.54",
      "type": "number"
    }
  },
  "required": [
    "name",
    "expression",
    "result"
  ],
  "additionalProperties": false
}

workspace_load

Load previously saved calculation result from workspace. Examples: load_variable("portfolio_return") # Returns saved calculation load_variable("circle_area") # Access across sessions

Parameters1

name

string

required

Name of the variable to load from workspace, e.g., 'circle_area'

Raw schema

{
  "type": "object",
  "properties": {
    "name": {
      "description": "Name of the variable to load from workspace, e.g., 'circle_area'",
      "type": "string"
    }
  },
  "required": [
    "name"
  ],
  "additionalProperties": false
}

plot_function

Generate mathematical function plots (requires matplotlib). Examples: plot_function("x**2", (-5, 5)) plot_function("sin(x)", (-3.14, 3.14))

Parameters3

expression	string	required	Mathematical expression to plot, e.g., "x2" or "sin(x)". Must be <= MAX_EXPRESSION_LENGTH characters. Example: "x2"
x_range	array	required	X-axis range as (min, max), e.g., (-5.0, 5.0)
num_points	integer	optional	Number of sample points to plot along x_range, e.g., 100

Raw schema

{
  "type": "object",
  "properties": {
    "expression": {
      "description": "Mathematical expression to plot, e.g., \"x**2\" or \"sin(x)\". Must be <= MAX_EXPRESSION_LENGTH characters. Example: \"x**2\"",
      "maxLength": 500,
      "type": "string"
    },
    "x_range": {
      "description": "X-axis range as (min, max), e.g., (-5.0, 5.0)",
      "maxItems": 2,
      "minItems": 2,
      "prefixItems": [
        {
          "type": "number"
        },
        {
          "type": "number"
        }
      ],
      "type": "array"
    },
    "num_points": {
      "default": 100,
      "description": "Number of sample points to plot along x_range, e.g., 100",
      "maximum": 10000,
      "minimum": 2,
      "type": "integer"
    }
  },
  "required": [
    "expression",
    "x_range"
  ],
  "additionalProperties": false
}

plot_histogram

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")

Parameters3

data	array	required	List of numeric values to bin, e.g., [1.0, 2.0, 2.5, 3.0]
bins	integer	optional	Number of histogram bins, e.g., 20
title	string	optional	Chart title string, e.g., 'Data Distribution'

Raw schema

{
  "type": "object",
  "properties": {
    "data": {
      "description": "List of numeric values to bin, e.g., [1.0, 2.0, 2.5, 3.0]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "bins": {
      "default": 20,
      "description": "Number of histogram bins, e.g., 20",
      "type": "integer"
    },
    "title": {
      "default": "Data Distribution",
      "description": "Chart title string, e.g., 'Data Distribution'",
      "maxLength": 100,
      "type": "string"
    }
  },
  "required": [
    "data"
  ],
  "additionalProperties": false
}

plot_line_chart

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')

Parameters7

x_data	array	required	X-axis data points, e.g., [1, 2, 3, 4]
y_data	array	required	Y-axis data points, e.g., [1, 4, 9, 16]
title	string	optional	Chart title string, e.g., 'Squares'
x_label	string	optional	X-axis label, e.g., 'Time'
y_label	string	optional	Y-axis label, e.g., 'Distance'
color	any	optional	Line color (name or hex code, e.g., 'blue', '#2E86AB')
show_grid	boolean	optional	Whether to display grid lines

Raw schema

{
  "type": "object",
  "properties": {
    "x_data": {
      "description": "X-axis data points, e.g., [1, 2, 3, 4]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "y_data": {
      "description": "Y-axis data points, e.g., [1, 4, 9, 16]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "title": {
      "default": "Line Chart",
      "description": "Chart title string, e.g., 'Squares'",
      "maxLength": 100,
      "type": "string"
    },
    "x_label": {
      "default": "X",
      "description": "X-axis label, e.g., 'Time'",
      "maxLength": 100,
      "type": "string"
    },
    "y_label": {
      "default": "Y",
      "description": "Y-axis label, e.g., 'Distance'",
      "maxLength": 100,
      "type": "string"
    },
    "color": {
      "anyOf": [
        {
          "maxLength": 100,
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Line color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "show_grid": {
      "default": true,
      "description": "Whether to display grid lines",
      "type": "boolean"
    }
  },
  "required": [
    "x_data",
    "y_data"
  ],
  "additionalProperties": false
}

plot_scatter

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)

Parameters7

x_data	array	required	X-axis data points, e.g., [1, 2, 3, 4]
y_data	array	required	Y-axis data points, e.g., [1, 4, 9, 16]
title	string	optional	Chart title string, e.g., 'Correlation Study'
x_label	string	optional	X-axis label, e.g., 'Variable X'
y_label	string	optional	Y-axis label, e.g., 'Variable Y'
color	any	optional	Point color (name or hex code, e.g., 'blue', '#2E86AB')
point_size	integer	optional	Scatter point size in points^2, e.g., 50

Raw schema

{
  "type": "object",
  "properties": {
    "x_data": {
      "description": "X-axis data points, e.g., [1, 2, 3, 4]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "y_data": {
      "description": "Y-axis data points, e.g., [1, 4, 9, 16]",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "type": "array"
    },
    "title": {
      "default": "Scatter Plot",
      "description": "Chart title string, e.g., 'Correlation Study'",
      "maxLength": 100,
      "type": "string"
    },
    "x_label": {
      "default": "X",
      "description": "X-axis label, e.g., 'Variable X'",
      "maxLength": 100,
      "type": "string"
    },
    "y_label": {
      "default": "Y",
      "description": "Y-axis label, e.g., 'Variable Y'",
      "maxLength": 100,
      "type": "string"
    },
    "color": {
      "anyOf": [
        {
          "maxLength": 100,
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Point color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "point_size": {
      "default": 50,
      "description": "Scatter point size in points^2, e.g., 50",
      "type": "integer"
    }
  },
  "required": [
    "x_data",
    "y_data"
  ],
  "additionalProperties": false
}

plot_box_plot

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")

Parameters5

data_groups	array	required	List of data groups to compare, e.g., [[1, 2, 3], [4, 5, 6]]
group_labels	any	optional	Labels for each group, e.g., ['Group A', 'Group B']
title	string	optional	Chart title string, e.g., 'Distribution Comparison'
y_label	string	optional	Y-axis label, e.g., 'Values'
color	any	optional	Box color (name or hex code, e.g., 'blue', '#2E86AB')

Raw schema

{
  "type": "object",
  "properties": {
    "data_groups": {
      "description": "List of data groups to compare, e.g., [[1, 2, 3], [4, 5, 6]]",
      "items": {
        "items": {
          "type": "number"
        },
        "type": "array"
      },
      "maxItems": 100,
      "type": "array"
    },
    "group_labels": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "maxItems": 100,
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Labels for each group, e.g., ['Group A', 'Group B']"
    },
    "title": {
      "default": "Box Plot",
      "description": "Chart title string, e.g., 'Distribution Comparison'",
      "maxLength": 100,
      "type": "string"
    },
    "y_label": {
      "default": "Values",
      "description": "Y-axis label, e.g., 'Values'",
      "maxLength": 100,
      "type": "string"
    },
    "color": {
      "anyOf": [
        {
          "maxLength": 100,
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Box color (name or hex code, e.g., 'blue', '#2E86AB')"
    }
  },
  "required": [
    "data_groups"
  ],
  "additionalProperties": false
}

plot_financial_line

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')

Parameters4

days	integer	optional	Number of days to generate, e.g., 30
trend	string	optional	Market trend direction
start_price	number	optional	Starting price value, e.g., 100.0
color	any	optional	Line color (name or hex code, e.g., 'blue', '#2E86AB')

Raw schema

{
  "type": "object",
  "properties": {
    "days": {
      "default": 30,
      "description": "Number of days to generate, e.g., 30",
      "maximum": 1000,
      "minimum": 2,
      "type": "integer"
    },
    "trend": {
      "default": "bullish",
      "description": "Market trend direction",
      "examples": [
        "bullish",
        "bearish",
        "volatile"
      ],
      "type": "string"
    },
    "start_price": {
      "default": 100,
      "description": "Starting price value, e.g., 100.0",
      "type": "number"
    },
    "color": {
      "anyOf": [
        {
          "maxLength": 100,
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README.md6 KBView on GitHub

math-mcp-learning-server

Educational MCP server with 17 tools, persistent workspace, and cloud hosting. Built with FastMCP and the official Model Context Protocol Python SDK.

Available on the MCP Registry (io.github.clouatre-labs/math-mcp-learning-server) and PyPI.

Demo

math-mcp Demo

See CONTRIBUTING.md for instructions to record your own demo.

Quick Start

Cloud (No Installation)

Connect your MCP client to the hosted server:

Claude Desktop (claude_desktop_config.json):

json

{
  "mcpServers": {
    "math-cloud": {
      "transport": "http",
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}

Local Installation

json

{
  "mcpServers": {
    "math": {
      "command": "uvx",
      "args": ["math-mcp-learning-server[scientific,plotting]"]
    }
  }
}

For other installation options (basic, scientific-only, plotting-only), see CONTRIBUTING.md.

Tools

Category	Tool	Description
Workspace	`workspace_save`	Save calculations to persistent storage
	`workspace_load`	Retrieve previously saved calculations
Math	`calc_expression`	Safely evaluate mathematical expressions
	`calc_statistics`	Statistical analysis (mean, median, mode, std_dev, variance)
	`calc_interest`	Calculate compound interest for investments
	`calc_units`	Convert between units (length, weight, temperature)
Matrix	`matrix_multiply`	Multiply two matrices
	`matrix_transpose`	Transpose a matrix
	`matrix_determinant`	Calculate matrix determinant
	`matrix_inverse`	Calculate matrix inverse
	`matrix_eigenvalues`	Calculate eigenvalues
Visualization	`plot_function`	Plot mathematical functions
	`plot_histogram`	Create statistical histograms
	`plot_line_chart`	Create line charts
	`plot_scatter`	Create scatter plots
	`plot_box_plot`	Create box plots
	`plot_financial_line`	Create financial line charts

Resources

math://workspace - Persistent calculation workspace summary
math://history - Chronological calculation history
math://functions - Available mathematical functions reference
math://constants/{constant} - Mathematical constants (pi, e, golden_ratio, etc.)
math://catalog/tools - Tool catalog with metadata and usage examples
math://variables - Active variables in the current workspace
math://test - Server health check

Prompts

math_tutor - Structured tutoring prompts (configurable difficulty)
formula_explainer - Formula explanation with step-by-step breakdowns

See Usage Examples for detailed examples.

Development

See CONTRIBUTING.md for development setup, testing, and contribution guidelines.

Security

OpenSSF Best Practices Silver - Fewer than 1% of open source projects reach this level
REUSE/SPDX - License compliance for all files
Signed Commits - GPG-signed commits required
Dependency Scanning - Automated updates via Renovate
pip-audit CVE Scanning - Automated dependency vulnerability checks
gitleaks Secret Scanning - Detects secrets in code and history
zizmor GitHub Actions Security - Workflow security scanning
commitlint Enforcement - Conventional commit validation in CI
OpenSSF Scorecard - Continuous open source security assessment

calc_expression safety

The calc_expression tool uses restricted eval() with a whitelist of allowed characters and functions, restricted global scope (only math module and abs), and no access to dangerous built-ins or imports. All tool inputs are validated with Pydantic models. File operations are restricted to the designated workspace directory. Complete type hints and validation are enforced for all operations.

Documentation

Install

Tools (17)

calc_expressioncalc_statisticscalc_interestcalc_unitsmatrix_multiplymatrix_transposematrix_determinantmatrix_inverse

Add to VS Code Add to Cursor

claude_desktop_config.json

json

{
  "mcpServers": {
    "math-mcp-learning-server": {
      "command": "uvx",
      "args": [
        "math-mcp-learning-server"
      ]
    }
  }
}

io.github.clouatre-labs/math-mcp-learning-server (MCP)

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

Topics

calc_expression

calc_statistics

calc_interest

calc_units

matrix_multiply

matrix_transpose

matrix_determinant

matrix_inverse

matrix_eigenvalues

workspace_save

workspace_load

plot_function

plot_histogram

plot_line_chart

plot_scatter

plot_box_plot

plot_financial_line

math-mcp-learning-server

Demo

Quick Start

Cloud (No Installation)

Local Installation

Tools

Resources

Prompts

Development

Security

Documentation

Install

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