Math Learning

{
  "id": "aix_680a29dd5c",
  "name": "Math Learning",
  "operator": "(unclaimed - source: pulsemcp · publisher: github.com)",
  "description": "Educational server for mathematical operations, statistics, and data visualization with persistent workspace",
  "depth": 1,
  "status": "unclaimed",
  "last_crawled": "2026-08-13",
  "missing_fields": [
    "pricing",
    "reputation",
    "aix_score",
    "operator.identity"
  ],
  "skills": [
    "market-data"
  ],
  "protocols": {
    "mcp": "https://math-mcp.fastmcp.app/mcp",
    "a2a": null
  },
  "pricing": null,
  "regions": [
    "global"
  ],
  "languages": [
    "en"
  ],
  "reputation": null,
  "aix_score": null,
  "verification": {
    "identity": "none",
    "health": "liveness-only",
    "pricing": "unknown",
    "last_check": "2026-08-13T21:00:38.275Z"
  },
  "pricing_model": "unknown",
  "links": [
    {
      "label": "homepage",
      "url": "https://www.pulsemcp.com/servers/clouatre-labs-math-learning"
    },
    {
      "label": "repository",
      "url": "https://github.com/clouatre-labs/math-mcp-learning-server"
    }
  ],
  "avatar": "https://github.com/clouatre-labs.png?size=160",
  "socials": [
    {
      "label": "github",
      "url": "https://github.com/clouatre-labs"
    }
  ],
  "public_stats": {
    "gh_stars": 5,
    "npm_downloads": 26414
  },
  "unreachable": false,
  "profile": {
    "mcp_server": "Math Learning Server",
    "mcp_version": "3.4.6",
    "tool_count": 17,
    "tools": [
      {
        "name": "calc_expression",
        "description": "Safely evaluate mathematical expressions with support for basic operations and math functions.\n\nSupported operations: +, -, *, /, **, ()\nSupported functions: sin, cos, tan, log, sqrt, abs, pow\n\nNote:\n    Use this tool to evaluate a single mathematical expression. To compute descriptive statistics over a list of numbers, use the statistics tool instead.\n\nExamples:\n- \"2 + 3 * 4\" → 14\n- \"sqrt(16)\" → 4.0\n- \"sin(3.14159/2)\" → 1.0"
      },
      {
        "name": "calc_statistics",
        "description": "Perform statistical calculations on a list of numbers.\n\nAvailable operations: mean, median, mode, std_dev, variance\n\nNote:\n    Use this tool to compute descriptive statistics over a list of numbers. To evaluate a single mathematical expression, use the calculate tool instead.\n\nExamples:\n    statistics([1.0, 2.5, 3.0, 4.5, 5.0], \"mean\")  # Returns 3.2\n    statistics([1.0, 2.5, 3.0, 4.5, 5.0], \"std_dev\")  # Returns ~1.58"
      },
      {
        "name": "calc_interest",
        "description": "Calculate compound interest for investments.\n\nFormula: A = P(1 + r/n)^(nt)\nWhere:\n- P = principal amount\n- r = annual interest rate (as decimal)\n- n = number of times interest compounds per year\n- t = time in years\n\nExamples:\n    compound_interest(10000, 0.05, 5)  # $10,000 at 5% for 5 years → $12,762.82\n    compound_interest(5000, 0.03, 10, 12)  # $5,000 at 3% compounded monthly → $6,744.25"
      },
      {
        "name": "calc_units",
        "description": "Convert between different units of measurement.\n\nSupported unit types:\n- length: mm, cm, m, km, in, ft, yd, mi\n- weight: g, kg, oz, lb\n- temperature: c, f, k (Celsius, Fahrenheit, Kelvin)\n\nExamples:\n    convert_units(5, \"km\", \"mi\", \"length\")  # 5 kilometers → 3.11 miles\n    convert_units(150, \"lb\", \"kg\", \"weight\")  # 150 pounds → 68.04 kilograms"
      },
      {
        "name": "matrix_multiply",
        "description": "Multiply two matrices (A × B).\n\nNote:\n    Requires NumPy. Raises ValueError if NumPy is unavailable.\n\nExamples:\n    matrix_multiply([[1, 2], [3, 4]], [[5, 6], [7, 8]])\n    matrix_multiply([[1, 2, 3]], [[1], [2], [3]])"
      },
      {
        "name": "matrix_transpose",
        "description": "Transpose a matrix (swap rows and columns).\n\nNote:\n    Requires NumPy. Raises ValueError if NumPy is unavailable.\n\nExamples:\n    matrix_transpose([[1, 2, 3], [4, 5, 6]])\n    matrix_transpose([[1], [2], [3]])"
      },
      {
        "name": "matrix_determinant",
        "description": "Calculate the determinant of a square matrix.\n\nNote:\n    Requires NumPy. Raises ValueError if NumPy is unavailable.\n\nExamples:\n    matrix_determinant([[1, 2], [3, 4]])\n    matrix_determinant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])  # Identity matrix"
      },
      {
        "name": "matrix_inverse",
        "description": "Calculate the inverse of a square matrix.\n\nNote:\n    Requires NumPy. Raises ValueError if NumPy is unavailable.\n\nExamples:\n    matrix_inverse([[1, 2], [3, 4]])\n    matrix_inverse([[2, 0], [0, 2]])  # Diagonal matrix"
      },
      {
        "name": "matrix_eigenvalues",
        "description": "Calculate the eigenvalues of a square matrix.\n\nNote:\n    Requires NumPy. Raises ValueError if NumPy is unavailable.\n\nExamples:\n    matrix_eigenvalues([[4, 2], [1, 3]])\n    matrix_eigenvalues([[3, 0, 0], [0, 5, 0], [0, 0, 7]])  # Diagonal matrix"
      },
      {
        "name": "workspace_save",
        "description": "Save calculation to persistent workspace (survives restarts).\n\nExamples:\n    save_calculation(\"portfolio_return\", \"10000 * 1.07^5\", 14025.52)\n    save_calculation(\"circle_area\", \"pi * 5^2\", 78.54)"
      },
      {
        "name": "workspace_load",
        "description": "Load previously saved calculation result from workspace.\n\nExamples:\n    load_variable(\"portfolio_return\")  # Returns saved calculation\n    load_variable(\"circle_area\")       # Access across sessions"
      },
      {
        "name": "plot_function",
        "description": "Generate mathematical function plots (requires matplotlib).\n\nExamples:\n    plot_function(\"x**2\", (-5, 5))\n    plot_function(\"sin(x)\", (-3.14, 3.14))"
      },
      {
        "name": "plot_histogram",
        "description": "Create statistical histograms (requires matplotlib).\n\nExamples:\n    plot_histogram([1.0, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0])\n    plot_histogram([10, 20, 30, 40, 50], bins=5, title=\"Test Scores\")"
      },
      {
        "name": "plot_line_chart",
        "description": "Create a line chart from data points (requires matplotlib).\n\nNote:\n    Use for general XY data. For time-series price data with optional moving average, use plot_financial_line instead.\n\nExamples:\n    plot_line_chart([1, 2, 3, 4], [1, 4, 9, 16], title=\"Squares\")\n    plot_line_chart([0, 1, 2], [0, 1, 4], color='red', x_label='Time', y_label='Distance')"
      },
      {
        "name": "plot_scatter",
        "description": "Create a scatter plot from data points (requires matplotlib).\n\nExamples:\n    plot_scatter([1, 2, 3, 4], [1, 4, 9, 16], title=\"Correlation Study\")\n    plot_scatter([1, 2, 3], [2, 4, 5], color='purple', point_size=100)"
      },
      {
        "name": "plot_box_plot",
        "description": "Create a box plot for comparing distributions (requires matplotlib).\n\nExamples:\n    plot_box_plot([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10]], group_labels=[\"A\", \"B\"])\n    plot_box_plot([[10, 20, 30], [15, 25, 35], [5, 15, 25]], title=\"Comparison\")"
      },
      {
        "name": "plot_financial_line",
        "description": "Generate and plot synthetic financial price data (requires matplotlib).\n\nCreates realistic price movement patterns for educational purposes.\nDoes not use real market data.\n\nNote:\n    Use for time-series price data with optional moving average overlay. For general XY data, use plot_line_chart instead.\n\nExamples:\n    plot_financial_line(days=60, trend='bullish')\n    plot_financial_line(days=90, trend='volatile', start_price=150.0, color='orange')"
      }
    ],
    "profiled_at": "2026-08-13T21:00:38.275Z"
  }
}