MCP Servers
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关于 MCP Servers
Open Source MCP Servers for Scientific Research
基本信息
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"
]
}
}
}工具
未检测到工具
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
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
FAQ from MCP Servers
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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