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AnishAchutha05

ARIA

by AnishAchutha05
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

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:

  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

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.txt

2. Configure API keys

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

Get keys from:

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

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

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

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)

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+

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 installed

F
license - not found
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quality - not tested
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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