# Math MCP Learning (remote · math-mcp.fastmcp.app)

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

- Trust score: 67/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `math-mcp.fastmcp.app`: 67/100 (this document), [markdown](https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp.md), [page](https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp)
- pypi · `math-mcp-learning-server`: 21/100, [markdown](https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp-learning-server.md), [page](https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp-learning-server)

## Channel facts

- Endpoint: `https://math-mcp.fastmcp.app/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.12.3`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 57/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 17 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS is enforced; there's no plaintext access path.
  - HSTS check failed: the Strict-Transport-Security header is absent.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 75/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 3275 tokens (~148/item across 22 items; 17 tools + 5 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (65% of tools); any adoption earns full credit.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add --transport http clouatre-labs-math-mcp-learning-server https://math-mcp.fastmcp.app/mcp
```

### Codex

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

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "clouatre-labs-math-mcp-learning-server": {
      "type": "remote",
      "url": "https://math-mcp.fastmcp.app/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add clouatre-labs-math-mcp-learning-server --url https://math-mcp.fastmcp.app/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  clouatre-labs-math-mcp-learning-server:
    url: "https://math-mcp.fastmcp.app/mcp"
```

### Other

```json
{
  "mcpServers": {
    "clouatre-labs-math-mcp-learning-server": {
      "type": "http",
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-02 (score 66, 0)

- [functional] Server version: 3.4.4 → 3.4.5

### 2026-08-01 (score 66, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 65, −1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 66, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-29 (score 65, +1)

No change was recorded against any check on this day. Stability & Change Management went from 7 to 10. That category is still filling its 30-day observation window: 2 days of observed history at the previous scan, 3 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 64, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 63)

First indexed and scored.

## MCP tools (17)

### `calc_expression` (~174 tokens)

Mathematical Calculator

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

Input parameters:

- `expression` (string, required): Mathematical expression to evaluate. Supports +, -, *, /, **, and math functions (sin, cos, sqrt, log, etc.). Example: '2 * sin(pi/4) + sqrt(16)'

Output parameters:

- `difficulty` (string)
- `expression` (string)
- `result` (number)
- `topic` (string)

### `calc_statistics` (~206 tokens)

Statistical Analysis

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

Input parameters:

- `numbers` (array, required): List of numbers to compute descriptive statistics on. Example: [1.0, 2.5, 3.0, 4.5, 5.0]
- `operation` (string, required): Statistical operation to perform. Allowed values: mean, median, mode, std_dev, variance

Output parameters:

- `difficulty` (string)
- `operation` (string)
- `result` (number)
- `sample_size` (integer)
- `topic` (string)

### `calc_interest` (~265 tokens)

Compound Interest Calculator

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

Input parameters:

- `compounds_per_year` (integer): Compounding frequency per year (must be > 0): 12=monthly, 365=daily
- `principal` (number, required): Initial investment amount in dollars (must be > 0), e.g. 1000.0
- `rate` (number, required): Annual interest rate as decimal 0.0-1.0 (e.g. 0.05 = 5%). If entering a percentage, divide by 100 first.
- `time` (number, required): Investment time in years (must be > 0), e.g. 10.0

Output parameters:

- `compounds_per_year` (integer)
- `difficulty` (string)
- `final_amount` (number)
- `formula` (string)
- `principal` (number)
- `rate` (number)
- `time` (number)
- `topic` (string)
- `total_interest` (number)

### `calc_units` (~265 tokens)

Unit Converter

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

Input parameters:

- `from_unit` (string, required): 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)
- `to_unit` (string, required): 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)
- `unit_type` (string, required): Unit category: length, weight, or temperature
- `value` (number, required): Numeric value to convert, e.g., 100.0

Output parameters:

- `converted_value` (number)
- `difficulty` (string)
- `from_unit` (string)
- `to_unit` (string)
- `topic` (string)
- `unit_type` (string)
- `value` (number)

### `matrix_multiply` (~163 tokens)

Matrix Multiplication

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]])

Input parameters:

- `matrix_a` (array, required): 2D list of numbers representing the first matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]
- `matrix_b` (array, required): 2D list of numbers representing the second matrix. Each inner list is a row. Example: [[5, 6], [7, 8]]

Output parameters:

- `cols_a` (integer)
- `cols_b` (integer)
- `difficulty` (string)
- `result_matrix` (array)
- `rows_a` (integer)
- `rows_b` (integer)
- `topic` (string)

### `matrix_transpose` (~115 tokens)

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]])

Input parameters:

- `matrix` (array, required): 2D list of numbers representing the matrix. Each inner list is a row. Example: [[1, 2, 3], [4, 5, 6]]

Output parameters:

- `difficulty` (string)
- `original_cols` (integer)
- `original_rows` (integer)
- `result_matrix` (array)
- `topic` (string)

### `matrix_determinant` (~128 tokens)

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

Input parameters:

- `matrix` (array, required): 2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]

Output parameters:

- `determinant` (number)
- `difficulty` (string)
- `size` (integer)
- `topic` (string)

### `matrix_inverse` (~108 tokens)

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

Input parameters:

- `matrix` (array, required): 2D list of numbers representing a square matrix. Each inner list is a row. Example: [[1, 2], [3, 4]]

Output parameters:

- `difficulty` (string)
- `error`
- `result_matrix`
- `size` (integer)
- `success` (boolean)
- `topic` (string)

### `matrix_eigenvalues` (~130 tokens)

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

Input parameters:

- `matrix` (array, required): 2D list of numbers representing a square matrix. Each inner list is a row. Example: [[4, 2], [1, 3]]

Output parameters:

- `complex_eigenvalues_warning`
- `complex_values`
- `difficulty` (string)
- `eigenvalues`
- `eigenvectors`
- `error`
- `size` (integer)
- `success` (boolean)
- `topic` (string)

### `workspace_save` (~136 tokens)

Save Calculation to Workspace

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)

Input parameters:

- `expression` (string, required): The mathematical expression that was evaluated. Example: 'pi * r**2'
- `name` (string, required): Variable name for the saved calculation. Used to retrieve it later. Example: 'circle_area'
- `result` (number, required): Numeric result of evaluating the expression, e.g., 78.54

Output parameters:

- `action` (string)
- `difficulty` (string)
- `expression` (string)
- `is_new` (boolean)
- `name` (string)
- `result` (number)
- `session_id`
- `success` (boolean)
- `topic` (string)
- `total_variables` (integer)

### `workspace_load` (~66 tokens)

Load Variable

Load previously saved calculation result from workspace.

Examples:
    load_variable("portfolio_return")  # Returns saved calculation
    load_variable("circle_area")       # Access across sessions

Input parameters:

- `name` (string, required): Name of the variable to load from workspace, e.g., 'circle_area'

Output parameters:

- `action` (string)
- `available_variables`
- `difficulty`
- `error`
- `expression`
- `name` (string)
- `result`
- `session_id`
- `success` (boolean)
- `timestamp`
- `topic`

### `plot_function` (~142 tokens)

Function Plotter

Generate mathematical function plots (requires matplotlib).

Examples:
    plot_function("x**2", (-5, 5))
    plot_function("sin(x)", (-3.14, 3.14))

Input parameters:

- `expression` (string, required): Mathematical expression to plot, e.g., "x**2" or "sin(x)". Must be <= MAX_EXPRESSION_LENGTH characters. Example: "x**2"
- `num_points` (integer): Number of sample points to plot along x_range, e.g., 100
- `x_range` (array, required): X-axis range as (min, max), e.g., (-5.0, 5.0)

### `plot_histogram` (~156 tokens)

Statistical 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")

Input parameters:

- `bins` (integer): Number of histogram bins, e.g., 20
- `data` (array, required): List of numeric values to bin, e.g., [1.0, 2.0, 2.5, 3.0]
- `title` (string): Chart title string, e.g., 'Data Distribution'

### `plot_line_chart` (~257 tokens)

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')

Input parameters:

- `color`: Line color (name or hex code, e.g., 'blue', '#2E86AB')
- `show_grid` (boolean): Whether to display grid lines
- `title` (string): Chart title string, e.g., 'Squares'
- `x_data` (array, required): X-axis data points, e.g., [1, 2, 3, 4]
- `x_label` (string): X-axis label, e.g., 'Time'
- `y_data` (array, required): Y-axis data points, e.g., [1, 4, 9, 16]
- `y_label` (string): Y-axis label, e.g., 'Distance'

### `plot_scatter` (~238 tokens)

Scatter Plot

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)

Input parameters:

- `color`: Point color (name or hex code, e.g., 'blue', '#2E86AB')
- `point_size` (integer): Scatter point size in points^2, e.g., 50
- `title` (string): Chart title string, e.g., 'Correlation Study'
- `x_data` (array, required): X-axis data points, e.g., [1, 2, 3, 4]
- `x_label` (string): X-axis label, e.g., 'Variable X'
- `y_data` (array, required): Y-axis data points, e.g., [1, 4, 9, 16]
- `y_label` (string): Y-axis label, e.g., 'Variable Y'

### `plot_box_plot` (~213 tokens)

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")

Input parameters:

- `color`: Box color (name or hex code, e.g., 'blue', '#2E86AB')
- `data_groups` (array, required): List of data groups to compare, e.g., [[1, 2, 3], [4, 5, 6]]
- `group_labels`: Labels for each group, e.g., ['Group A', 'Group B']
- `title` (string): Chart title string, e.g., 'Distribution Comparison'
- `y_label` (string): Y-axis label, e.g., 'Values'

### `plot_financial_line` (~174 tokens)

Financial Line Chart

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')

Input parameters:

- `color`: Line color (name or hex code, e.g., 'blue', '#2E86AB')
- `days` (integer): Number of days to generate, e.g., 30
- `start_price` (number): Starting price value, e.g., 100.0
- `trend` (string): Market trend direction

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp#diagnostics

## Score history

- 2026-08-03: 67
- 2026-08-02: 66
- 2026-08-01: 66
- 2026-07-31: 65
- 2026-07-30: 66
- 2026-07-29: 65
- 2026-07-28: 64
- 2026-07-27: 64
- 2026-07-26: 63

## Links

- Remote endpoint: https://math-mcp.fastmcp.app/mcp
- Repository: https://github.com/clouatre-labs/math-mcp-learning-server
- Changelog RSS feed: https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/clouatre-labs-math-mcp-learning-server/math-mcp