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mcp-scientific-rag
A high-performance, 100% local Retrieval-Augmented Generation (RAG) system built on the Model Context Protocol (MCP). Designed specifically to help AI agents process complex scientific literature with unprecedented accuracy.
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Python
llmlocal-aimcpmodel-context-protocolollamapdf-extractionpythonragscientific-researchvector-database
Quality signals
ActiveNo testsNo CIThin docsNo licenseNew
InstallMCPRun via uvx (Python)
Universal installANY TOOL
$ npx skillcity@latest davinson-pezo/mcp-scientific-ragAuto-detects the skill or MCP server in this repo and installs it for Claude Code, Codex, Cursor, Cline, OpenCode, Gemini CLI, Anti-Gravity, OpenClaw and Hermes.
Requires Node.js 18+ (ships with npx).
Or install manually
uvx mcp-scientific-rag
MCP Configuration
Add this to your claude_desktop_config.json
{
"mcpServers": {
"mcp-scientific-rag": {
"command": "uvx",
"args": [
"mcp-scientific-rag"
]
}
}
}Loading README…
Ask a simulated agent to run this skill. It loads the real SKILL.md and reasons over your task with NVIDIA.
mcp-scientific-rag — sandboxAI sandbox
Pick a quick prompt below or type your own — watch the agent execute this skill step by step.
guest@skillcity:~$Try a prompt