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Math Mcp Learning

Educational MCP server for math operations, statistics, visualization, and persistent workspaces. Built with FastMCP 2.0.

data-science-tools
By clouatre-labs
55Updated 1 week agoPythonNOASSERTION

Installation

npx -y math-mcp-learning-server

Configuration

{
  "mcpServers": {
    "math-mcp-learning-server": {
      "command": "npx",
      "args": ["-y", "math-mcp-learning-server"]
    }
  }
}

How to use

  1. Run the installation command above (if needed)
  2. Open your Claude Code settings file (~/.claude/settings.json)
  3. Add the configuration to the mcpServers section
  4. Restart Claude Code to apply changes
<!-- mcp-name: io.github.clouatre-labs/math-mcp-learning-server --> <h1 align="center">math-mcp-learning-server</h1> <p align="center"> <a href="https://pypi.org/project/math-mcp-learning-server/"><img alt="PyPI" src="https://img.shields.io/pypi/v/math-mcp-learning-server?style=for-the-badge&color=3b82f6" height="20"></a> <a href="https://pypi.org/project/math-mcp-learning-server/"><img alt="Python" src="https://img.shields.io/pypi/pyversions/math-mcp-learning-server?style=for-the-badge" height="20"></a> <a href="LICENSE"><img alt="License" src="https://img.shields.io/badge/license-Apache--2.0-blue.svg?style=for-the-badge" height="20"></a> <a href="https://api.reuse.software/info/github.com/clouatre-labs/math-mcp-learning-server"><img alt="REUSE" src="https://img.shields.io/reuse/compliance/github.com/clouatre-labs/math-mcp-learning-server?style=for-the-badge" height="20"></a> <a href="https://www.bestpractices.dev/projects/12334"><img alt="OpenSSF Best Practices" src="https://img.shields.io/cii/level/12334?style=for-the-badge" height="20"></a> </p> <p align="center"><strong>Educational MCP server with 17 tools, persistent workspace, and cloud hosting.</strong> Built with <a href="https://gofastmcp.com">FastMCP</a> and the official <a href="https://github.com/modelcontextprotocol/python-sdk">Model Context Protocol Python SDK</a>.</p> <p align="center"> Available on the <a href="https://registry.modelcontextprotocol.io/">MCP Registry</a> (<code>io.github.clouatre-labs/math-mcp-learning-server</code>) and <a href="https://pypi.org/project/math-mcp-learning-server/">PyPI</a>. </p>

Demo

math-mcp Demo

See CONTRIBUTING.md for instructions to record your own demo.

Quick Start

Cloud (No Installation)

Connect your MCP client to the hosted server:

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "math-cloud": {
      "transport": "http",
      "url": "https://math-mcp.fastmcp.app/mcp"
    }
  }
}

Local Installation

{
  "mcpServers": {
    "math": {
      "command": "uvx",
      "args": ["math-mcp-learning-server[scientific,plotting]"]
    }
  }
}

For other installation options (basic, scientific-only, plotting-only), see CONTRIBUTING.md.

Tools

CategoryToolDescription
Workspaceworkspace_saveSave calculations to persistent storage
workspace_loadRetrieve previously saved calculations
Mathcalc_expressionSafely evaluate mathematical expressions
calc_statisticsStatistical analysis (mean, median, mode, std_dev, variance)
calc_interestCalculate compound interest for investments
calc_unitsConvert between units (length, weight, temperature)
Matrixmatrix_multiplyMultiply two matrices
matrix_transposeTranspose a matrix
matrix_determinantCalculate matrix determinant
matrix_inverseCalculate matrix inverse
matrix_eigenvaluesCalculate eigenvalues
Visualizationplot_functionPlot mathematical functions
plot_histogramCreate statistical histograms
plot_line_chartCreate line charts
plot_scatterCreate scatter plots
plot_box_plotCreate box plots
plot_financial_lineCreate financial line charts

Resources

  • math://workspace - Persistent calculation workspace summary
  • math://history - Chronological calculation history
  • math://functions - Available mathematical functions reference
  • math://constants/{constant} - Mathematical constants (pi, e, golden_ratio, etc.)
  • math://catalog/tools - Tool catalog with metadata and usage examples
  • math://variables - Active variables in the current workspace
  • math://test - Server health check

Prompts

  • math_tutor - Structured tutoring prompts (configurable difficulty)
  • formula_explainer - Formula explanation with step-by-step breakdowns

See Usage Examples for detailed examples.

Development

See CONTRIBUTING.md for development setup, testing, and contribution guidelines.

Security

  • OpenSSF Best Practices Silver - Fewer than 1% of open source projects reach this level
  • REUSE/SPDX - License compliance for all files
  • Signed Commits - GPG-signed commits required
  • Dependency Scanning - Automated updates via Renovate
  • pip-audit CVE Scanning - Automated dependency vulnerability checks
  • gitleaks Secret Scanning - Detects secrets in code and history
  • zizmor GitHub Actions Security - Workflow security scanning
  • commitlint Enforcement - Conventional commit validation in CI
  • OpenSSF Scorecard - Continuous open source security assessment
<details> <summary><strong>calc_expression safety</strong></summary>

The calc_expression tool uses restricted eval() with a whitelist of allowed characters and functions, restricted global scope (only math module and abs), and no access to dangerous built-ins or imports. All tool inputs are validated with Pydantic models. File operations are restricted to the designated workspace directory. Complete type hints and validation are enforced for all operations.

</details>

Documentation

View source on GitHub