math-mcp

# Math MCP Server ## Overview This is an MCP (Model Context Protocol) server that can do basic arithmetic in 64 bit precision, along with matrix multiplication. <a href="https://glama.ai/mcp/servers/@avanishd-3/math-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@avanishd-3/math-mcp/badge" alt="Math Server MCP server" /> </a> ## Features - Addition - Subtraction - Multiplication - Division - Matrix multiplication ## Installation ### Prerequisities Ensure you have the following installed: - Python 3.13+ - uv #### Installing UV See [installation guide](https://docs.astral.sh/uv/getting-started/installation/#standalone-installer) for all options. ##### Unix/MacOS ``` curl -LsSf https://astral.sh/uv/install.sh | sh ``` or ``` brew install uv ``` ##### Windows ``` winget install --id=astral-sh.uv -e ``` ### Clone the Repository ``` git clone https://github.com/avanishd-3/math-mcp.git cd math-mcp-server uv sync ``` ## Integration with Clients ### Claude Code fastmcp install claude-code src/math_server.py:math_mcp ### Claude Desktop fastmcp install claude-desktop src/math_server.py:math_mcp ### Cursor fastmcp install cursor src/math_server.py:math_mcp ### VS Code Add the following .vscode/mcp.json and use your actual path. ``` { "servers": { "Math MCP Server": { "command": "uv", "args": [ "run", "--with", "fastmcp", "--with", "numpy", "fastmcp", "run", "/absolute/path/Desktop/to/math-mcp-server/src/math_server.py:math_mcp" ] } }, } ``` ## Contributing 1. Fork the repository 2. Create a new branch: ``` git checkout -b add-feature ``` 3. Make changes and commit (remember to add unit tests in test/ directory) ``` git commit -m "Added a new feature" ``` 4. Push to your fork ``` git push origin add-feature ``` 5. Open a pull request. ## Project Structure ```text / ├── src │ └── math_server.py ├── tests │   ├── test_arithmetic.py │   │   └── astro.svg │   ├── test_linear_algebra.py ├── pytest.ini ├── pyproject.toml └── uv.lock ``` ### Architecture This MCP server uses [Fast MCP 2.0](https://gofastmcp.com/getting-started/welcome), which provides many more features than Fast MCP 1.0, which is what the official Python SDK for MCP uses. Also, the unit tests are written with Pytest, which is what Fast MCP 2.0 recommends. Lastly, if you don't know, uv is a much faster version of pip that also provides a lockfile for project dependencies (this will be familiar if you've used npm or cargo before). The MCP Python SDK itself uses uv, and I use it for all new Python projects, because it's 10-100x faster than pip, and the lockfile makes dependency version management much simpler.