Aria Mcp Research Server — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Aria Mcp Research Server (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
Give ARIA a topic → it autonomously:
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.txtcp .env.example .env
# Open .env and fill in your keysGet keys from:
Open your Claude Desktop config file:
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.jsonAdd 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.
cd client
python aria_client.py "federated learning in healthcare"
python aria_client.py "transformer architecture" 3aria-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# 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| 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) |
| 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+ |
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.
MIT
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.