Quick Start

Running any server in this repository is as simple as a single command. For example, to start the web-fetch server:

uvx mcp-science web-fetch

This command handles everything from installation to execution. For more details on configuration and finding other servers, see the "How to configure MCP servers for AI client apps" section below.

Table of Contents

• About
• What is MCP?
• Available servers in this repo
• How to integrate MCP servers into LLM
• How to build your own MCP server
• Contributing
• License
• Acknowledgments
• Citation

About

This repository contains a collection of open source MCP servers specifically designed for scientific research applications. These servers enable Al models (like Claude) to interact with scientific data, tools, and resources through a standardized protocol.

What is MCP?

MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.

MCP helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:

• A growing list of pre-built integrations that your LLM can directly plug into
• The flexibility to switch between LLM providers and vendors
• Best practices for securing your data within your infrastructure

Source: https://modelcontextprotocol.io/introduction

Available servers in this repo

Below is a complete list of the MCP servers that live in this monorepo. Every entry links to the sub-directory that contains the server’s source code and README so that you can find documentation and usage instructions quickly.

Example Server

An example MCP server that demonstrates the minimal pieces required for a server implementation.

Materials Project

A specialised MCP server that enables AI assistants to search, visualise and manipulate materials-science data from the Materials Project database. A Materials Project API key is required.

Python Code Execution

Runs Python code snippets in a secure, sandboxed environment with restricted standard-library access so that assistants can carry out analysis and computation without risking your system.

SSH Exec

Allows an assistant to run pre-validated commands on remote machines over SSH with configurable authentication and command whitelists.

Web Fetch

Fetches and processes HTML, PDF and plain-text content from the Web so that the assistant can quote or summarise it.

TXYZ Search

Performs Web, academic and “best effort” searches via the TXYZ API. A TXYZ API key is required.

Timer

A minimal countdown timer that streams progress updates to demonstrate MCP notifications.

GPAW Computation

Provides density-functional-theory (DFT) calculations through the GPAW package.

[Jupyter-Act](./servers/ju

... View full README on GitHub