https://github.com/colesmcintosh/numpy-mcp
A Model Context Protocol (MCP) server for numerical computations with NumPy
https://github.com/colesmcintosh/numpy-mcp
ai anthropic claude mcp mcp-server
Last synced: 7 days ago
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A Model Context Protocol (MCP) server for numerical computations with NumPy
- Host: GitHub
- URL: https://github.com/colesmcintosh/numpy-mcp
- Owner: colesmcintosh
- Created: 2025-03-04T05:10:26.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-05-19T16:10:52.000Z (about 1 month ago)
- Last Synced: 2025-05-19T17:27:27.551Z (about 1 month ago)
- Topics: ai, anthropic, claude, mcp, mcp-server
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- mcp-index - NumPy Calculator - Perform numerical computations using NumPy, including basic arithmetic operations and linear algebra tasks such as matrix multiplication and statistical analysis. Interface seamlessly with LLMs through a standardized MCP protocol. (Math and Statistics)
README
[](https://mseep.ai/app/colesmcintosh-numpy-mcp)
# NumPy MCP Server
A Model Context Protocol (MCP) server for numerical computations with NumPy
[![MIT licensed][mit-badge]][mit-url]
[mit-badge]: https://img.shields.io/badge/license-MIT-blue.svg
[mit-url]: ./LICENSE
[python-badge]: https://img.shields.io/badge/python-3.8%2B-blue.svg
[python-url]: https://www.python.org/downloads/
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:
```bash
# Install the server in Claude Desktop
mcp install server.py --name "NumPy Calculator"
```
### Manual Installation
This project uses UV for dependency management. To install:
```bash
# 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:
```bash
mcp dev server.py
```
### Claude Desktop Integration
1. Install the server in Claude Desktop:
```bash
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:
```bash
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