Integrates with Hugging Face Spaces to provide a web-based Streamlit interface for browsing datasets and testing queries on plant species, small molecules, and mitochondrial inhibitor data.
Provides access to PubMed literature data, enabling searches for research papers on mitochondrial Complex I inhibitors and natural compounds, with tools to generate PubMed URLs from PMIDs and extract MeSH tags from scientific literature.
title: Aurora-MCP emoji: 🌿 colorFrom: green colorTo: indigo sdk: docker pinned: false
🌿 Aurora-MCP
Model Context Protocol (MCP) server providing access to datasets of natural and synthetic small molecules, with a focus on identifying potential mitochondrial Complex I inhibitors that may occur in plant species.
🔍 Overview
Aurora-MCP is a Model Context Protocol (MCP) server and data integration layer that connects natural-product, biodiversity, and mitochondrial-inhibitor datasets. It enables LLMs and users to query relationships between plant species, small molecules, and mitochondrial Complex I inhibition—bridging COCONUT, Laji.fi, GBIF, and AI-derived PubMed data through structured joins and metadata schemas.
Aurora-MCP is a lightweight MCP server + Hugging Face Space designed to bridge two complementary knowledge sources:
🌿 Aurora — natural-product and plant biodiversity data, mapping compounds to genera and species found in Nordic ecosystems.
🧬 Aurora-Mito-ETL — curated PubMed-derived corpus of small-molecule inhibitors of mitochondrial Complex I (NADH dehydrogenase).
Together they form a conversational dataset where ChatGPT (or any MCP-compatible LLM) can reason over structured biological data, ask questions, and perform targeted searches on small compounds, plants, and mechanistic links between them.
🧠 Concept
Goal: allow scientific dialogue with an LLM grounded in domain data, for example:
“Show me plant-derived compounds that inhibit mitochondrial Complex I.”
“Find PubMed evidence for arctigenin as a Complex I inhibitor.”
“List Nordic plants whose metabolites overlap with known ETC inhibitors.”
Aurora-MCP turns your static text/TSV data into an interactive semantic backend, exposing programmatic tools for searching, linking, and reasoning.
a FastAPI‑based MCP endpoint (/mcp) that ChatGPT (or any MCP‑aware client)
can connect to. It also provides /healthz for status checks and simple debug HTTP routes
for local testing.
🚀 Quick start (local)
You should see something like:
🧠 Using with ChatGPT (MCP)
Deploy this repository to a Hugging Face Space (Docker SDK).
Wait until the Space is running and
/healthzreturns 200 OK:https://huggingface.co/spaces/<you>/<space>/healthzIn ChatGPT → Settings → Connectors / MCP → Add Server
Server URL:
https://huggingface.co/spaces/<you>/<space>/mcp
Open a new chat and try for example:
list_files(path="data")read_text(path="README.md")(Aurora domain tools can be added similarly.)
🐳 Docker (for Hugging Face Spaces)
🧩 Architecture overview
Component | Description |
FastAPI app | Hosts the
streaming endpoint and
check |
FastMCP | MCP server layer that exposes Python functions as MCP tools |
Tools | Simple functions (
,
, etc.) that can be called by MCP clients |
Aurora domain | (Future) plant‑compound and inhibitor analytics from your Aurora ETL data |
📂 Project layout
✅ Health & debug routes
Endpoint | Purpose |
| lightweight JSON health check |
| list directory contents (no MCP) |
| read a file as plain text |
⚙️ Requirements
Install with:
🧱 Hugging Face Space metadata
🔍 Troubleshooting
Symptom | Cause / Fix |
→ 307/500 | Normal; only MCP clients can connect |
| Fixed by FastMCP startup hook |
| Install correct SDK:
|
returns nothing | Curl 127.0.0.1 not 0.0.0.0 |
Author: Daniel Nicorici · University of Helsinki
License: GNU GPL v3
URL: https://github.com/ndaniel/aurora-mcp
This server cannot be installed