Math MCP Learning MCP Server

io.github.clouatre-labs/math-mcp-learning-server

answering

Math MCP Learning is answering right now. Last checked 2 min ago. 790 installs a week from pypi. It exposes 17 tools. Last commit 4 Aug 2026.

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

Installs per day peak 459 · avg 70 · +68% w/w

a month agotoday

Uptime history 43 hours of history

43 hours agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

550 ms

Response time

average over 24h

790

Installs / week

npm and PyPI

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 2 min ago.

Claude Code Claude Desktop Codex Cursor VS Code

run in your terminal

claude mcp add math-mcp-learning-server --transport http https://math-mcp.fastmcp.app/mcp

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "math-mcp-learning-server": {
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}

~/.codex/config.toml

[mcp_servers.math-mcp-learning-server]
url = "https://math-mcp.fastmcp.app/mcp"

.cursor/mcp.json

{
  "mcpServers": {
    "math-mcp-learning-server": {
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}

.vscode/mcp.json

{
  "mcpServers": {
    "math-mcp-learning-server": {
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}

Available tools 17

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

plot

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")

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')

plot_function

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

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")

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')

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)

matrix

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

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

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

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]])

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]])

calc

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

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

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

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

workspace

workspace_load

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

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)

Endpoints

URL	Transport	State	Latency	Checked
https://math-mcp.fastmcp.app/mcp	streamable-http	answering	559 ms	2 min ago

Math MCP Learning — questions

Answers built from our own checks of this server.

What can Math MCP Learning do?

It exposes 17 tools, read directly from the server on our last check. Among them: calc_expression, calc_interest, calc_statistics, calc_units, matrix_determinant, matrix_eigenvalues and 11 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →

What is Math MCP Learning mostly used for?

Its tools cluster around plot, matrix and calc. That is what this server is built to work with — the grouping comes from the actual tool names, not from a category we assigned.

Is Math MCP Learning working right now?

We send a real MCP handshake every 15 minutes. Over the last 24 hours 91 of 91 checks got a reply (100.0%), average response time 550 ms. The bar chart above shows every period we have measured.

How do I connect Math MCP Learning?

Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address.

Does Math MCP Learning need an API key?

No. Math MCP Learning completed a full MCP handshake with us as an anonymous client and listed its tools without asking for anything. All 17 of them are readable on this page. This is what we observed, not what the docs claim.

How fast is Math MCP Learning?

It answers our handshake in 550 ms on average, which is faster than 19% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.

How many people use Math MCP Learning?

The pypi package math-mcp-learning-server was installed 790 times in the last week. Week over week that is +68%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.

Is Math MCP Learning open source?

Yes — it is published under the NOASSERTION licence, written in Python, 5 stars on GitHub and 1 open issue. The source link is on this page, so you can read exactly what it does with your data before you connect it.