MCP Servers

Name: MCP Servers
Author: pathintegral-institute

@pathintegral-institute

リポジトリホームページ

MCP Servers について

Open Source MCP Servers for Scientific Research

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

基本情報

カテゴリ

その他

ライセンス

MIT

ランタイム

python

トランスポート

stdio

公開者

pathintegral-institute

設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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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