Math MCP Learning MCP Server

by clouatre-labs Your server? Claim it

answering

Math MCP Learning is answering right now. Last checked 8 min ago. 686 installs a week from pypi. It exposes 17 tools. Last commit 17 Sep 2026.

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

Installs per day peak 305 · avg 80 · +15% w/w

a month agotoday

Uptime history 47 days of history

47 days agonow

100.0%

Uptime 24h

91 of 91 checks

Tools

read from the server

555 ms

Response time

average over 24h

686

Installs / week

npm and PyPI

What changed 3

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 15 August 2026. No other catalogue keeps this.

17 Sep a tool changed version

5 Sep a tool changed version

15 Aug a tool changed version

What the code does

We read the source, 21 h ago · tools taken from the live server · rules 3dff92dd89df

Capabilities

What this server is able to do. For an MCP server this is often the job itself — a terminal server runs commands because that is what it is for. Listed so you know what you are plugging in, not as an accusation.

Executes code built at runtime src/math_mcp/eval.py:73, [пакет] src/math_mcp/eval.py:73

        result = eval(safe_expr, allowed_globals, {})

Is this your server and something here is wrong? Tell us — corrections are free and do not require a plan.

This code can reach further than it looks

We found places where it runs commands, builds paths or queries from values it is given. None of that is a flaw by itself — it becomes one when the code changes, and code changes quietly between releases. We re-read it on every one.

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 8 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	515 ms	8 min ago

Alternatives to Math MCP Learning

same job, measured the same way

Memori MCP

by memorilabs

Memori MCP server — persistent AI memory with recall and augmentation tools

107 installs/wk local only

sayou

by pixell-global

Persistent knowledge workspace for AI agents. Versioned files, search, and MCP tools.

52 installs/wk local only

SageMath

by xbp-europe

Stateful SageMath MCP server with 40 tools and a sandboxed Sage session per client.

113 installs/wk local only

Gmail

by pulsemcp

MCP server for Gmail with OAuth2 (personal) and service account (Workspace) support.

144 installs/wk local only

OMEGA Memory

by omega-memory

Persistent memory, coordination, and learning for AI agents. Local-first, 25 MCP tools.

1 167 installs/wk local only

Hex SSH

by levnikolaevich

Token-efficient SSH MCP with hash-verified files and persistent tmux sessions. 14 tools.

63 installs/wk local only

Kody

by kentcdodds

Personal assistant MCP server with search, execute, packages, jobs, secrets, and integrations.

answering

Toggl 2.0

by toggl

Toggl 2.0 MCP server for tasks, projects, time entries, and workspace operations.

2 230 installs/wk local only

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 555 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 555 ms on average, which is faster than 24% 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 686 times in the last week. Week over week that is +15%. 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?

We cannot say either way: written in Python, 5 stars on GitHub and 1 open issue, but we could not determine the licence, and without one the code is not open source by default.