Math MCP Learning

★ 4

from clouatre-labs

Educational MCP server with math operations, statistics, visualizations, and persistent workspace.

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math-mcp-learning-server

Educational MCP server with 17 tools, persistent workspace, and cloud hosting. Built with FastMCP and the official Model Context Protocol Python SDK.

Available on the MCP Registry (io.github.clouatre-labs/math-mcp-learning-server) and PyPI.

Demo

$math-mcp Demo$

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

Tools

Category	Tool	Description
Workspace	`workspace_save`	Save calculations to persistent storage
	`workspace_load`	Retrieve previously saved calculations
Math	`calc_expression`	Safely evaluate mathematical expressions
	`calc_statistics`	Statistical analysis (mean, median, mode, std_dev, variance)
	`calc_interest`	Calculate compound interest for investments
	`calc_units`	Convert between units (length, weight, temperature)
Matrix	`matrix_multiply`	Multiply two matrices
	`matrix_transpose`	Transpose a matrix
	`matrix_determinant`	Calculate matrix determinant
	`matrix_inverse`	Calculate matrix inverse
	`matrix_eigenvalues`	Calculate eigenvalues
Visualization	`plot_function`	Plot mathematical functions
	`plot_histogram`	Create statistical histograms
	`plot_line_chart`	Create line charts
	`plot_scatter`	Create scatter plots
	`plot_box_plot`	Create box plots
	`plot_financial_line`	Create 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

calc_expression safety

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.

Documentation

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.