ARIA
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ARIAResearch the topic: quantum computing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ARIA — Autonomous Research & Intelligence Assistant
An MCP server that autonomously researches any topic: searches the web, scrapes sources, extracts insights, builds a knowledge graph, and synthesizes a structured research brief — in under 90 seconds.
What It Does
Give ARIA a topic → it autonomously:
Searches the web for relevant sources (Tavily API)
Scrapes and cleans full page content (httpx + BeautifulSoup)
Extracts key concepts, claims, and gaps from each source (Claude API)
Builds a NetworkX knowledge graph of connected concepts
Synthesizes a final research brief with citations
Related MCP server: OpenDeepSearch
Setup
1. Clone & create virtual environment
git clone https://github.com/YOUR_USERNAME/aria-mcp.git
cd aria-mcp
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt2. Configure API keys
cp .env.example .env
# Open .env and fill in your keysGet keys from:
Anthropic API: https://console.anthropic.com
Tavily API: https://tavily.com (free tier works)
3. Connect to Claude Desktop
Open your Claude Desktop config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
Add the ARIA server (replace the path with your actual absolute path):
{
"mcpServers": {
"aria": {
"command": "python",
"args": ["/absolute/path/to/aria-mcp/server/main.py"]
}
}
}Restart Claude Desktop. ARIA will appear as an available MCP tool.
4. Or use the CLI client
cd client
python aria_client.py "federated learning in healthcare"
python aria_client.py "transformer architecture" 3Project Structure
aria-mcp/
├── server/
│ ├── main.py ← MCP server entry point (integration)
│ ├── tools/
│ │ ├── search.py ← Tavily web search
│ │ ├── scraper.py ← httpx + BeautifulSoup scraper
│ │ ├── summarizer.py ← Claude-powered insight extraction
│ │ └── graph.py ← NetworkX knowledge graph
│ └── utils/
│ └── helpers.py ← Shared utilities
├── client/
│ └── aria_client.py ← CLI demo client
├── tests/
│ ├── test_search.py
│ ├── test_scraper.py
│ ├── test_summarizer.py
│ └── test_graph.py
├── output/ ← Research JSON results (gitignored)
├── .env.example
├── .gitignore
├── claude_desktop_config.json ← Claude Desktop config snippet
├── requirements.txt
└── README.mdTesting Individual Modules
# From project root, with venv activated
python tests/test_search.py
python tests/test_scraper.py
python tests/test_summarizer.py
python tests/test_graph.pyTeam Split
Person | File | Responsibility |
Person 1 |
| Web search via Tavily |
Person 2 |
| URL scraping + text extraction |
Person 3 |
| Claude-powered summarization + synthesis |
Person 4 |
| Knowledge graph construction |
All together |
| MCP server integration (Day 2) |
Tech Stack
Layer | Tool |
MCP Framework |
|
LLM | Claude Sonnet via Anthropic API |
Web Search | Tavily API |
Web Scraping | httpx + BeautifulSoup4 |
Knowledge Graph | NetworkX |
Language | Python 3.11+ |
Demo
In Claude Desktop, type:
"Research the topic: Federated Learning in IoT devices"
ARIA will autonomously search 5 sources, scrape them, summarize each, build a knowledge graph, and produce a full research brief — all in real time.
License
MIT
This server cannot be deployed
Maintenance
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