What is Numpy Mcp

numpy-mcp is a Model Context Protocol (MCP) server designed for performing numerical computations using the NumPy library. It provides a standardized interface for mathematical operations, making it easy to integrate with Claude or other MCP-compatible systems.

Use cases

Use cases for numpy-mcp include calculating eigenvalues of matrices, performing statistical analysis on datasets, and fitting polynomials to data points for regression analysis.

How to use

To use numpy-mcp, install it in Claude Desktop by running ‘mcp install server.py --name “NumPy Calculator”’. You can also run it directly using Python or through the MCP Inspector for development testing.

Key features

Key features include basic arithmetic operations, linear algebra computations (such as matrix multiplication and eigendecomposition), statistical analysis (mean, median, standard deviation, min, max), and polynomial fitting.

Where to use

numpy-mcp can be used in various fields such as data science, machine learning, academic research, and any domain requiring numerical computations and mathematical analysis.

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