Awesome-Scientific s

Awesome Scientific MCP is a curated directory of Model Context Protocol (MCP) servers focused on scientific discovery. This repository collects specialized MCP services across disciplines so AI agents can connect to external tools, data sources, and workflows in a standardized way. Use this as a reference to discover existing MCP endpoints and understand the kinds of capabilities available (e.g., data access, simulations, scientific computations, literature search). To integrate with an AI agent or framework, refer to the MCP transport formats described in your tooling (SSE, HTTP, etc.) and connect to the appropriate server endpoint using the provider’s recommended workflow. Since this is a catalog rather than a single runnable server, you’ll typically rely on the individual server repositories linked in the list to operate and configure each MCP service.

Prerequisites:

• git
• Node.js or Python environment if you plan to run individual MCP servers from their repositories
• Access to the internet to clone the relevant MCP server repositories

Step-by-step:

1. Clone this repository (the MCP server catalog): git clone https://github.com/defense-seu-awesome-scientific-mcp-servers/defense-seu-awesome-scientific-mcp-servers.git cd defense-seu-awesome-scientific-mcp-servers

2. Review the individual MCP server repos linked in the list to follow their specific installation instructions. Each server may have different requirements (language, dependencies, and runtime).

3. For each MCP server you wish to run, follow the repository’s installation guide. Typical steps may include:

   • Install dependencies (e.g., npm install or pip install -r requirements.txt)
   • Build or compile any components if required
   • Configure environment variables as documented by the server (e.g., API keys, database URLs)
   • Start the server (e.g., node server.js or uvicorn app:app --reload or docker run ...)

4. Verify connectivity by querying the server’s MCP endpoint using its documented transport (SSE, HTTP) and ensure it responds to basic MCP requests.

5. Integrate with your AI workflow by pointing your MCP-enabled tools to the server endpoints as described in your MCP tooling docs.

Notes:

• This README serves as a directory of MCP servers rather than a single runnable service. To run a specific server, follow its individual repository's setup instructions.
• Some servers may expose HTTP endpoints while others use SSE streaming. Choose the transport that matches your AI tooling (e.g., Claude Code, other MCP clients).
• When contributing, maintain alphabetical order and follow the contribution format shown in the README.
• If you plan to deploy multiple MCP servers, consider containerization (Docker) or a package management approach documented by each server.
• Common issues include missing environment variables, API keys, or incompatible dependency versions. Check each server’s docs for required ENV vars and defaults.