mcp.science
mcp.scienceは、Pythonを使用して科学的計算やデータ分析を行うためのライブラリです。使いやすいAPIを提供し、複雑な数値計算を簡素化します。特に、データの可視化や解析に強みを持ち、研究者やデータサイエンティストにとって便利なツールです。
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閲覧数
36
フォーク
19
イシュー
3
インストール方法
難易度
上級推定所要時間
20-45 分
インストール方法
uv pip install mcpm
mcpm client ls # discover supported clients
mcpm client set # pick the one you are using
mcpm add web-fetch
``
After the command finishes, restart your client so that it reloads its tool
configuration. You can browse the [MCPM Registry](https://mcpm.sh/registry/)
for additional community-maintained servers.
Please check [How to build your own MCP server step by step](./docs/how-to-build-your-own-mcp-server-step-by-step.md) for more details.
We enthusiastically welcome contributions to MCP.science! You can help with improving the existing servers, adding new servers, or anything that you think will make this project better.
If you are not familiar with GitHub and how to contribute to a open source repository, then it might be a bit of challenging, but it's still easy for you. We would recommend you to read these first:
[How to make your first pull request on GitHub](https://www.freecodecamp.org/news/how-to-make-your-first-pull-request-on-github-3/)
[Creating a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request?tool=webui)
In short, you can follow these steps:
1Fork the repository to your own GitHub account
2Clone the forked repository to your local machine
3Create a feature branch (git checkout -b feature/amazing-feature)
4Make your changes and commit them (git commit -m 'Add amazing feature'`)
👈 Click to see more conventions about directory and naming