Math MCP Learning is a remote MCP server that gives an AI agent 17 tools, among them calc_expression, calc_interest, calc_statistics. It answers at https://math-mcp.fastmcp.app/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 6 of 6 times since 11 September 2026; the last check, on 21 October 2026, succeeded.
Educational MCP server with 17 math/stats tools, visualizations, and persistent workspace
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
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 mathema Inputs: expression |
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_interes Inputs: principal, rate, time, compounds_per_year |
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, u Inputs: numbers, operation |
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 Inputs: value, from_unit, to_unit, unit_type |
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 Inputs: 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 Inputs: 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 Inputs: 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]]) Inputs: matrix_a, matrix_b |
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]]) Inputs: matrix |
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") Inputs: data_groups, group_labels, title, y_label, color |
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 ove Inputs: days, trend, start_price, color |
plot_function | Generate mathematical function plots (requires matplotlib). Examples: plot_function("x**2", (-5, 5)) plot_function("sin(x)", (-3.14, 3.14)) Inputs: expression, x_range, num_points |
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") Inputs: data, bins, title |
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], titl Inputs: x_data, y_data, title, x_label, y_label, color |
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) Inputs: x_data, y_data, title, x_label, y_label, color |
workspace_load | Load previously saved calculation result from workspace. Examples: load_variable("portfolio_return") # Returns saved calculation load_variable("circle_area") # Access across sessions Inputs: name |
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) Inputs: name, expression, result |
Every block below points at https://math-mcp.fastmcp.app/mcp. Pick your client and copy.
claude mcp add --transport http math-mcp-learning https://math-mcp.fastmcp.app/mcp
{
"mcpServers": {
"math-mcp-learning": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://math-mcp.fastmcp.app/mcp"]
}
}
}{
"mcpServers": {
"math-mcp-learning": {
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"servers": {
"math-mcp-learning": {
"type": "http",
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"context_servers": {
"math-mcp-learning": {
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"mcpServers": {
"math-mcp-learning": {
"serverUrl": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"mcpServers": {
"math-mcp-learning": {
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}name: Math MCP Learning
version: 0.0.1
schema: v1
mcpServers:
- name: Math MCP Learning
type: streamable-http
url: https://math-mcp.fastmcp.app/mcp{
"mcpServers": {
"math-mcp-learning": {
"httpUrl": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"mcpServers": {
"math-mcp-learning": {
"type": "http",
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}{
"mcpServers": {
"math-mcp-learning": {
"url": "https://math-mcp.fastmcp.app/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fmath-mcp.fastmcp.app%2Fmcp&type=streamable_http&id=math-mcp-learning&name=Math%20MCP%20Learning
It answered our last check on 21 October 2026 in 438 ms. Over 6 checks since 11 September 2026 it answered 6. Status here comes from Neuronto's own probes, not from the publisher.
17, as returned by the server's own tools/list on 13 September 2026: calc_expression, calc_interest, calc_statistics, calc_units, matrix_determinant, matrix_eigenvalues, matrix_inverse, matrix_multiply, and 9 more.
It answered our last check on 21 October 2026 in 438 ms. Over 6 checks since 11 September 2026 it answered 6.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://math-mcp.fastmcp.app/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: io.github.clouatre-labs. Endpoint host: math-mcp.fastmcp.app. ARD identifier: urn:air:io.github.clouatre-labs:mcp:math-mcp-learning-server.
First indexed 10 September 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.