_ registry / mcp http-sse · checked 1h ago

Math Learning Server

https://math-mcp.fastmcp.app

Registry code: 92c0606e1300fb95

api record

Math Learning Server - use these tools for mathematical computation:

CALCULATE: Use `calculate` for arithmetic/algebra, `statistics` for lists, `compound_interest` for finance (rate as decimal: 0.05 = 5%), `convert_units` for unit conversion (length/weight/temperature).

endpoint
https://math-mcp.fastmcp.app/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
1,743ms

last good check

priced tools
0

of 17 tools

_ answered our checks, 90 days 2 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 17 tools
1 open 16 never probed 1 of 17 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • plot_financial_line open 3h ago

    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')

    mcp-tool

    {
  "type": "object",
  "properties": {
    "days": {
      "type": "integer",
      "default": 30,
      "maximum": 1000,
      "minimum": 2,
      "description": "Number of days to generate, e.g., 30"
    },
    "color": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 100
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Line color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "trend": {
      "type": "string",
      "default": "bullish",
      "examples": [
        "bullish",
        "bearish",
        "volatile"
      ],
      "description": "Market trend direction"
    },
    "start_price": {
      "type": "number",
      "default": 100,
      "description": "Starting price value, e.g., 100.0"
    }
  },
  "additionalProperties": false
}
    arguments 41 lines
  • matrix_eigenvalues unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "matrix"
  ],
  "properties": {
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[4, 2], [1, 3]]"
    }
  },
  "additionalProperties": false
}
    arguments 20 lines
  • workspace_load unknown never probed

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

    mcp-tool

    {
  "type": "object",
  "required": [
    "name"
  ],
  "properties": {
    "name": {
      "type": "string",
      "description": "Name of the variable to load from workspace, e.g., 'circle_area'"
    }
  },
  "additionalProperties": false
}
    arguments 13 lines
  • calc_expression unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "expression"
  ],
  "properties": {
    "expression": {
      "type": "string",
      "maxLength": 500,
      "description": "Mathematical expression to evaluate. Supports +, -, *, /, **, and math functions (sin, cos, sqrt, log, etc.). Example: '2 * sin(pi/4) + sqrt(16)'"
    }
  },
  "additionalProperties": false
}
    arguments 14 lines
  • calc_statistics unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "numbers",
    "operation"
  ],
  "properties": {
    "numbers": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "List of numbers to compute descriptive statistics on. Example: [1.0, 2.5, 3.0, 4.5, 5.0]"
    },
    "operation": {
      "type": "string",
      "examples": [
        "mean",
        "median",
        "mode",
        "std_dev",
        "variance"
      ],
      "description": "Statistical operation to perform. Allowed values: mean, median, mode, std_dev, variance"
    }
  },
  "additionalProperties": false
}
    arguments 29 lines
  • calc_interest unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "principal",
    "rate",
    "time"
  ],
  "properties": {
    "rate": {
      "type": "number",
      "maximum": 1,
      "minimum": 0,
      "description": "Annual interest rate as decimal 0.0-1.0 (e.g. 0.05 = 5%). If entering a percentage, divide by 100 first."
    },
    "time": {
      "type": "number",
      "description": "Investment time in years (must be > 0), e.g. 10.0",
      "exclusiveMinimum": 0
    },
    "principal": {
      "type": "number",
      "description": "Initial investment amount in dollars (must be > 0), e.g. 1000.0",
      "exclusiveMinimum": 0
    },
    "compounds_per_year": {
      "type": "integer",
      "default": 12,
      "description": "Compounding frequency per year (must be > 0): 12=monthly, 365=daily",
      "exclusiveMinimum": 0
    }
  },
  "additionalProperties": false
}
    arguments 33 lines
  • calc_units unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "value",
    "from_unit",
    "to_unit",
    "unit_type"
  ],
  "properties": {
    "value": {
      "type": "number",
      "description": "Numeric value to convert, e.g., 100.0"
    },
    "to_unit": {
      "type": "string",
      "examples": [
        "ft",
        "lb",
        "f"
      ],
      "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)"
    },
    "from_unit": {
      "type": "string",
      "examples": [
        "m",
        "kg",
        "c"
      ],
      "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)"
    },
    "unit_type": {
      "type": "string",
      "examples": [
        "length",
        "weight",
        "temperature"
      ],
      "description": "Unit category: length, weight, or temperature"
    }
  },
  "additionalProperties": false
}
    arguments 43 lines
  • matrix_multiply unknown never probed

    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]])

    mcp-tool

    {
  "type": "object",
  "required": [
    "matrix_a",
    "matrix_b"
  ],
  "properties": {
    "matrix_a": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing the first matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]"
    },
    "matrix_b": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing the second matrix. Each inner list is a row. Example: [[5, 6], [7, 8]]"
    }
  },
  "additionalProperties": false
}
    arguments 32 lines
  • matrix_transpose unknown never probed

    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]])

    mcp-tool

    {
  "type": "object",
  "required": [
    "matrix"
  ],
  "properties": {
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing the matrix. Each inner list is a row. Example: [[1, 2, 3], [4, 5, 6]]"
    }
  },
  "additionalProperties": false
}
    arguments 20 lines
  • matrix_determinant unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "matrix"
  ],
  "properties": {
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]"
    }
  },
  "additionalProperties": false
}
    arguments 20 lines
  • matrix_inverse unknown never probed

    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

    mcp-tool

    {
  "type": "object",
  "required": [
    "matrix"
  ],
  "properties": {
    "matrix": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 10000,
      "description": "2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]"
    }
  },
  "additionalProperties": false
}
    arguments 20 lines
  • workspace_save unknown never probed

    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)

    mcp-tool

    {
  "type": "object",
  "required": [
    "name",
    "expression",
    "result"
  ],
  "properties": {
    "name": {
      "type": "string",
      "maxLength": 50,
      "description": "Variable name for the saved calculation. Used to retrieve it later. Example: 'circle_area'"
    },
    "result": {
      "type": "number",
      "description": "Numeric result of evaluating the expression, e.g., 78.54"
    },
    "expression": {
      "type": "string",
      "maxLength": 500,
      "description": "The mathematical expression that was evaluated. Example: 'pi * r**2'"
    }
  },
  "additionalProperties": false
}
    arguments 25 lines
  • plot_function unknown never probed

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

    mcp-tool

    {
  "type": "object",
  "required": [
    "expression",
    "x_range"
  ],
  "properties": {
    "x_range": {
      "type": "array",
      "maxItems": 2,
      "minItems": 2,
      "description": "X-axis range as (min, max), e.g., (-5.0, 5.0)",
      "prefixItems": [
        {
          "type": "number"
        },
        {
          "type": "number"
        }
      ]
    },
    "expression": {
      "type": "string",
      "maxLength": 500,
      "description": "Mathematical expression to plot, e.g., \"x**2\" or \"sin(x)\". Must be <= MAX_EXPRESSION_LENGTH characters. Example: \"x**2\""
    },
    "num_points": {
      "type": "integer",
      "default": 100,
      "maximum": 10000,
      "minimum": 2,
      "description": "Number of sample points to plot along x_range, e.g., 100"
    }
  },
  "additionalProperties": false
}
    arguments 36 lines
  • plot_histogram unknown never probed

    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")

    mcp-tool

    {
  "type": "object",
  "required": [
    "data"
  ],
  "properties": {
    "bins": {
      "type": "integer",
      "default": 20,
      "description": "Number of histogram bins, e.g., 20"
    },
    "data": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "List of numeric values to bin, e.g., [1.0, 2.0, 2.5, 3.0]"
    },
    "title": {
      "type": "string",
      "default": "Data Distribution",
      "maxLength": 100,
      "description": "Chart title string, e.g., 'Data Distribution'"
    }
  },
  "additionalProperties": false
}
    arguments 28 lines
  • plot_line_chart unknown never probed

    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')

    mcp-tool

    {
  "type": "object",
  "required": [
    "x_data",
    "y_data"
  ],
  "properties": {
    "color": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 100
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Line color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "title": {
      "type": "string",
      "default": "Line Chart",
      "maxLength": 100,
      "description": "Chart title string, e.g., 'Squares'"
    },
    "x_data": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "X-axis data points, e.g., [1, 2, 3, 4]"
    },
    "y_data": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "Y-axis data points, e.g., [1, 4, 9, 16]"
    },
    "x_label": {
      "type": "string",
      "default": "X",
      "maxLength": 100,
      "description": "X-axis label, e.g., 'Time'"
    },
    "y_label": {
      "type": "string",
      "default": "Y",
      "maxLength": 100,
      "description": "Y-axis label, e.g., 'Distance'"
    },
    "show_grid": {
      "type": "boolean",
      "default": true,
      "description": "Whether to display grid lines"
    }
  },
  "additionalProperties": false
}
    arguments 62 lines
  • plot_scatter unknown never probed

    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)

    mcp-tool

    {
  "type": "object",
  "required": [
    "x_data",
    "y_data"
  ],
  "properties": {
    "color": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 100
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Point color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "title": {
      "type": "string",
      "default": "Scatter Plot",
      "maxLength": 100,
      "description": "Chart title string, e.g., 'Correlation Study'"
    },
    "x_data": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "X-axis data points, e.g., [1, 2, 3, 4]"
    },
    "y_data": {
      "type": "array",
      "items": {
        "type": "number"
      },
      "maxItems": 10000,
      "description": "Y-axis data points, e.g., [1, 4, 9, 16]"
    },
    "x_label": {
      "type": "string",
      "default": "X",
      "maxLength": 100,
      "description": "X-axis label, e.g., 'Variable X'"
    },
    "y_label": {
      "type": "string",
      "default": "Y",
      "maxLength": 100,
      "description": "Y-axis label, e.g., 'Variable Y'"
    },
    "point_size": {
      "type": "integer",
      "default": 50,
      "description": "Scatter point size in points^2, e.g., 50"
    }
  },
  "additionalProperties": false
}
    arguments 62 lines
  • plot_box_plot unknown never probed

    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")

    mcp-tool

    {
  "type": "object",
  "required": [
    "data_groups"
  ],
  "properties": {
    "color": {
      "anyOf": [
        {
          "type": "string",
          "maxLength": 100
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Box color (name or hex code, e.g., 'blue', '#2E86AB')"
    },
    "title": {
      "type": "string",
      "default": "Box Plot",
      "maxLength": 100,
      "description": "Chart title string, e.g., 'Distribution Comparison'"
    },
    "y_label": {
      "type": "string",
      "default": "Values",
      "maxLength": 100,
      "description": "Y-axis label, e.g., 'Values'"
    },
    "data_groups": {
      "type": "array",
      "items": {
        "type": "array",
        "items": {
          "type": "number"
        }
      },
      "maxItems": 100,
      "description": "List of data groups to compare, e.g., [[1, 2, 3], [4, 5, 6]]"
    },
    "group_labels": {
      "anyOf": [
        {
          "type": "array",
          "items": {
            "type": "string"
          },
          "maxItems": 100
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Labels for each group, e.g., ['Group A', 'Group B']"
    }
  },
  "additionalProperties": false
}
    arguments 61 lines
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