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.
MCP Servers
@pathintegral-institute
Basic information
About MCP Servers
Open Source MCP Servers for 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
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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