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:

Manual Installation

This project uses UV for dependency management. To install:

Usage

Development Testing

Test the server locally with the MCP Inspector:

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:

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

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