NumPy MCP Server

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

1. Install the server in Claude Desktop:

   mcp install server.py --name "NumPy Calculator"

2. The server will now be available in Claude Desktop under "NumPy Calculator"

3. 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