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MCP Servers

@pathintegral-institute

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Basic information

Category

Other

License

MIT

Transports

stdio

Author

pathintegral-institute

About MCP Servers

Open Source MCP Servers for Scientific Research

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

MCP Servers config

Add this server to your MCP-compatible client using the configuration below.

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

MCP Servers tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

Fetch the live tool list by running this server in a temporary sandbox using the button above.

Overview

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

Frequently asked questions

1

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.

2

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.

3

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.

4

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/`.

5

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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