NumPy MCP Server

A Model Context Protocol (MCP) server for numerical computations with NumPy

A Model Context Protocol (MCP) server that provides mathematical calculations and operations using NumPy. This server exposes various mathematical tools through a standardized MCP interface, making it easy to perform numerical computations directly through Claude or other MCP-compatible LLMs.

Features

Basic arithmetic operations (addition)
Linear algebra computations (matrix multiplication, eigendecomposition)
Statistical analysis (mean, median, standard deviation, min, max)
Polynomial fitting

Installation

Quick Setup with Claude Desktop

The fastest way to get started is to install this server directly in Claude Desktop:

# Install the server in Claude Desktop
mcp install server.py --name "NumPy Calculator"

Manual Installation

This project uses UV for dependency management. To install:

# Install UV if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/math-mcp.git
cd math-mcp

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Unix/macOS
# or
# .venv\Scripts\activate  # On Windows
uv pip install -r requirements.txt

Usage

Development Testing

Test the server locally with the MCP Inspector:

mcp dev server.py

Claude Desktop Integration

Install the server in Claude Desktop:
mcp install server.py --name "NumPy Calculator"
The server will now be available in Claude Desktop under "NumPy Calculator"
You can use it by asking Claude to perform mathematical operations, for example:
- "Calculate the eigenvalues of matrix [[1, 2], [3, 4]]"
- "Find the mean and standard deviation of [1, 2, 3, 4, 5]"
- "Multiply matrices [[1, 0], [0, 1]] and [[2, 3], [4, 5]]"

Direct Execution

For advanced usage or custom deployments:

python server.py
# or
mcp run server.py

Available Functions

The server provides the following mathematical functions through the MCP interface:

Basic Arithmetic

add(a: int, b: int) -> int: Add two integers together

Linear Algebra

matrix_multiply(matrix_a: List[List[float]], matrix_b: List[List[float]]) -> List[List[float]]: Multiply two matrices
eigen_decomposition(matrix: List[List[float]]) -> Tuple[List[float], List[List[float]]]: Compute eigenvalues and eigenvectors of a square matrix

Statistics

statistical_analysis(data: List[float]) -> dict[str, float]: Calculate basic statistics for a dataset including:
- Mean
- Median
- Standard deviation
- Minimum value
- Maximum value

Data Analysis

polynomial_fit(x: List[float], y: List[float], degree: int = 2) -> List[float]: Fit a polynomial of specified degree to the given data points

Development

Project Structure

math-mcp/
├── requirements.txt
├── README.md
└── server.py

Code Quality

This project adheres to strict code quality standards:

Type hints throughout the codebase
Comprehensive docstrings following Google style
Error handling for numerical operations

Dependencies

NumPy: For numerical computations and linear algebra operations
FastMCP: For Model Context Protocol server implementation

License

This project is licensed under the MIT License.

Acknowledgments

NumPy team for their excellent scientific computing library
Model Context Protocol (MCP) for enabling standardized LLM interactions

NumPy Calculator

Integrations