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.

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What It Does

Give ARIA a topic → it autonomously:

1. Searches the web for relevant sources (Tavily API)
2. Scrapes and cleans full page content (httpx + BeautifulSoup)
3. Extracts key concepts, claims, and gaps from each source (Claude API)
4. Builds a NetworkX knowledge graph of connected concepts
5. Synthesizes a final research brief with citations

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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.txt

2. Configure API keys

cp .env.example .env
# Open .env and fill in your keys

Get 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.json
• Windows: %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" 3

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Project 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.md

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Testing 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.py

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Team Split

Person	File	Responsibility
Person 1	tools/search.py	Web search via Tavily
Person 2	tools/scraper.py	URL scraping + text extraction
Person 3	tools/summarizer.py	Claude-powered summarization + synthesis
Person 4	tools/graph.py	Knowledge graph construction
All together	server/main.py	MCP server integration (Day 2)

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Tech Stack

Layer	Tool
MCP Framework	mcp Python SDK by Anthropic
LLM	Claude Sonnet via Anthropic API
Web Search	Tavily API
Web Scraping	httpx + BeautifulSoup4
Knowledge Graph	NetworkX
Language	Python 3.11+

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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.

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License

MIT