math-mcp

A small Model Context Protocol (MCP) server that gives an LLM a set of reliable math tools — expression evaluation, statistics, number theory, linear algebra, and symbolic math — instead of asking it to do arithmetic in its head.

Every tool runs real, deterministic code. The expression evaluator is sandboxed: it parses input into an AST and only allows a whitelist of operators, constants, and functions. It never calls Python's eval. Symbolic tools parse input with SymPy's tokenizing parser (also no eval).

Tools

Numeric & number theory

Linear algebra

Matrices are lists of rows, e.g. [[1, 2], [3, 4]].

Symbolic (SymPy)

Resources & prompts

Beyond tools, the server exposes:

• Resources — mathematical constants to 50 digits: math://constants (an index) and math://constants/{name} for pi, e, tau, phi (golden ratio) and gamma (Euler–Mascheroni).
• Prompts — reusable templates a client can offer to the user: solve_step_by_step and solve_linear_system_prompt.

Install

Requires Python 3.10+.

Once published to PyPI (distribution name math-mcp-ay):

pip install math-mcp-ay

The import package is math_mcp and the command is math-mcp regardless of the distribution name.

From source (for development):

git clone https://github.com/AbderY/math-mcp.git
cd math-mcp
pip install -e ".[dev]"

Run

The server speaks MCP over stdio:

math-mcp
# or
python -m math_mcp

Use with an MCP client

Add it to your client's MCP configuration. For example (Claude Desktop / claude_desktop_config.json, or any MCP client that launches servers):

{
  "mcpServers": {
    "math": {
      "command": "math-mcp"
    }
  }
}

If math-mcp is not on your PATH, use the interpreter form instead:

{
  "mcpServers": {
    "math": {
      "command": "python",
      "args": ["-m", "math_mcp"]
    }
  }
}

Examples

• evaluate("2 ** 10 + factorial(5)") → 1144.0
• descriptive_statistics([2, 4, 4, 4, 5, 5, 7, 9]) → mean 5.0, pstdev 2.0
• prime_factorization(360) → 2³ · 3² · 5
• solve_quadratic(1, -3, 2) → roots 1.0 and 2.0
• convert_base("ff", 16, 2) → "11111111"
• matrix_inverse([[4, 7], [2, 6]]) → [[0.6, -0.7], [-0.2, 0.4]]
• solve_linear_system([[1, 1], [1, -1]], [3, 1]) → [2.0, 1.0]
• differentiate("x**3", "x", 2) → "6*x"
• definite_integral("exp(-x)", "x", "0", "oo") → "1"
• limit("sin(x)/x", "x", "0") → "1"
• taylor_series("exp(x)", "x", "0", 4) → "x**3/6 + x**2/2 + x + 1"
• solve_symbolic_equation("x**2 = 4") → ["-2", "2"]

Development

pip install -e ".[dev]"
pytest

Releasing to PyPI

Publishing is automated via GitHub Actions using PyPI Trusted Publishing (OIDC), so no API token is stored in the repo.

One-time setup on pypi.org:

1. Create (or claim) the project name math-mcp-ay.
2. Under the project's Publishing settings, add a trusted publisher:
   • Owner: AbderY, repository: math-mcp
   • Workflow: publish.yml
   • Environment: pypi

Then, to release: bump the version in pyproject.toml and src/math_mcp/__init__.py, tag it, and publish a GitHub Release. The publish.yml workflow builds the sdist + wheel and uploads them to PyPI.

License

MIT