MCP Servers

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基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

pathintegral-institute

关于 MCP Servers

Open Source MCP Servers for Scientific Research

#mcp-servers #open-source#scientific-research

MCP Servers 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-servers": {
      "command": "uvx",
      "args": [
        "mcp-science",
        "web-fetch"
      ]
    }
  }
}

MCP Servers 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is MCP Servers?

MCP Servers is a monorepo collection of open-source MCP (Model Context Protocol) servers designed for scientific research applications. They enable AI models to interact with scientific data, tools, and resources through a standardized protocol.

How to use MCP Servers?

Install uv and an MCP‑enabled client (e.g. Claude Desktop, VSCode, Goose, 5ire). Launch any server with uvx mcp-science <server-name> (e.g. uvx mcp-science web-fetch). Optionally use the mcpm tool to automate client configuration.

Key features of MCP Servers

Open-source collection built for scientific research
Standardized MCP protocol for AI–data integration
Single‑command launch via uvx mcp-science
Covers materials science, web fetch, Python execution, SSH, DFT, and more
Works with multiple MCP‑enabled clients (Claude Desktop, VSCode, Goose, 5ire)
Packaged as a Python monorepo on PyPI (mcp-science)

Use cases of MCP Servers

Search and visualise materials‑science data from the Materials Project
Fetch and summarise web content (HTML, PDF, plain text)
Execute Python code in a sandboxed environment for safe analysis
Run pre‑validated commands on remote machines over SSH
Perform density‑functional‑theory (DFT) calculations via GPAW
Interact programmatically with a running Jupyter kernel

常见问题

What is MCP?

MCP is an open protocol that standardises how applications provide context to LLMs, similar to a USB‑C port for AI. It allows models to integrate with various data sources and tools in a consistent way.

What are the prerequisites for using MCP Servers?

You need `uv` (a fast Python package manager) and an MCP‑enabled client such as Claude Desktop, VSCode, Goose, or 5ire.

How do I run a server?

Use the command `uvx mcp-science <server-name>` (e.g. `uvx mcp-science web-fetch`). This downloads the `mcp-science` package from PyPI and launches the server.

Can I build my own MCP server?

Yes. The repository includes a step‑by‑step guide under `docs/how-to-build-your-own-mcp-server-step-by-step.md` and an example server in `servers/example-server/`.

How can I contribute to MCP Servers?

Fork the repository, create a feature branch, make changes with clear commit messages, update documentation, add tests, and open a pull request. Follow the naming conventions described in the contributing guide.

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